Results for “human performance data” — Page 54

NASA NTRS2002

Influence of vestibular activation on respiration in humans

The purpose of this study was to determine the effects of the semicircular canals and otolith organs on respiration in humans. On the basis of animal studies, we hypothesized that vestibular activation would elicit a vestibulorespiratory reflex. To test this hypothesis, respiratory measures, arterial blood pressure, and heart rate were measured during engagement of semicircular canals and/or otolith organs. Dynamic upright pitch and roll (15 cycles/min), which activate the otolith organs and semicircular canals, increased respiratory rate (Delta2 +/- 1 and Delta3 +/- 1 breaths/min, respectively; P < 0.05). Dynamic yaw and lateral pitch (15 cycles/min), which activate the semicircular canals, increased respiration similarly (Delta3 +/- 1 and Delta2 +/- 1, respectively; P < 0.05). Dynamic chair rotation (15 cycles/min), which mimics dynamic yaw but eliminates neck muscle afferent, increased respiration (Delta3 +/- 1; P < 0.05) comparable to dynamic yaw (15 cycles/min). Increases in respiratory rate were graded as greater responses occurred during upright (Delta5 +/- 2 breaths/min) and lateral pitch (Delta4 +/- 1) and roll (Delta5 +/- 1) performed at 30 cycles/min. Increases in breathing frequency resulted in increases in minute ventilation during most interventions. Static head-down rotation, which activates otolith organs, did not alter respiratory rate (Delta1 +/- 1 breaths/min). Collectively, these data indicate that semicircular canals, but not otolith organs or neck muscle afferents, mediate increased ventilation in humans and support the concept that vestibular activation alters respiration in humans.

NASA Discipline Neuroscience↗

NASA NTRSDate not supplied

Analysis of Alternative Architectures for a 2024 Lunar Sortie

This study focused on performing a broad analysis of lunar architectures to identify key alternatives capable of performing a 2024 human lunar landing mission. The goal of the study was to identify architecture alternatives which exhibit robustness, as measured by launch vehicle payload margin, to absorb the natural growth that occurs during design maturation. The study consisted of up to three element architectures which perform the mission from cis-lunar aggregation, through the surface sortie, and return to cis-lunar space. More than 600,000 data points were evaluated utilizing a novel architecture synthesis framework currently being developed by NASA/MSFC. Key findings from the study indicate that architecture complexity can be reduced by minimizing the number of discrete, unique architecture elements, which in turn translates into reduced design, development, test, and evaluation (DDT&E) complexity. It was identified that heavy lift launch vehicles enable such architectures while also providing significant increases to launch vehicle margin over existing and near-term commercial launch vehicle (CLV) performance expected in the 2024 time frame. Additionally, the study identified storable propellant based lander architectures as leading alternatives in achieving a 2024 human lunar landing by providing reduced technology needs and development schedules.

Architecture Analysis↗

NASA NTRS2014

Human Exploration Science Office (KX) Overview

The Human Exploration Science Office supports human spaceflight, conducts research, and develops technology in the areas of space orbital debris, hypervelocity impact technology, image science and analysis, remote sensing, imagery integration, and human and robotic exploration science. NASA's Orbital Debris Program Office (ODPO) resides in the Human Exploration Science Office. ODPO provides leadership in orbital debris research and the development of national and international space policy on orbital debris. The office is recognized internationally for its measurement and modeling of the debris environment. It takes the lead in developing technical consensus across U.S. agencies and other space agencies on debris mitigation measures to protect users of the orbital environment. The Hypervelocity Impact Technology (HVIT) project evaluates the risks to spacecraft posed by micrometeoroid and orbital debris (MMOD). HVIT facilities at JSC and White Sands Test Facility (WSTF) use light gas guns, diagnostic tools, and high-speed imagery to quantify the response of spacecraft materials to MMOD impacts. Impact tests, with debris environment data provided by ODPO, are used by HVIT to predict risks to NASA and commercial spacecraft. HVIT directly serves NASA crew safety with MMOD risk assessments for each crewed mission and research into advanced shielding design for future missions. The Image Science and Analysis Group (ISAG) supports the International Space Station (ISS) and commercial spaceflight through the design of imagery acquisition schemes (ground- and vehicle-based) and imagery analyses for vehicle performance assessments and mission anomaly resolution. ISAG assists the Multi-Purpose Crew Vehicle (MPCV) Program in the development of camera systems for the Orion spacecraft that will serve as data sources for flight test objectives that lead to crewed missions. The multi-center Imagery Integration Team is led by the Human Exploration Science Office and provides expertise in the application of engineering imagery to spaceflight. The team links NASA programs and private industry with imagery capabilities developed and honed through decades of human spaceflight, including imagery integration, imaging assets, imagery data management, and photogrammetric analysis. The team is currently supporting several NASA programs, including commercial demonstration missions. The Earth Science and Remote Sensing Team is responsible for integrating the scientific use of Earth-observation assets onboard the ISS, which consist of externally mounted sensors and crew photography capabilities. This team facilitates collaboration on remote sensing and participates in research with academic organizations and other Government agencies, not only in conjunction with ISS science, but also for planetary exploration and regional environmental/geological studies. Human exploration science focuses on science strategies for future human exploration missions to the Moon, Mars, asteroids, and beyond. This function provides communication and coordination between the science community and mission planners. ARES scientists support the operation of robotic missions (i.e., Mars Exploration Rovers and the Mars Science Laboratory), contribute to the interpretation of returned mission data, and translate robotic mission technologies and techniques to human spaceflight.

Calhoun, Tracy A.↗

DOE OSTI2021

Landscape rules predict optimal superhighways for the first peopling of Sahul

Archaeological data and demographic modelling suggest that the peopling of Sahul required substantial populations, occurred rapidly within a few thousand years and encompassed environments ranging from hyper-arid deserts to temperate uplands and tropical rainforests. How this migration occurred and how humans responded to the physical environments they encountered have, however, remained largely speculative. By constructing a high-resolution digital elevation model for Sahul and coupling it with fine-scale viewshed analysis of landscape prominence, least-cost pedestrian travel modelling and high-performance computing, here we create over 125 billion potential migratory pathways, whereby the most parsimonious routes traversed emerge. Our analysis revealed several major pathways—superhighways—transecting the continent, that we evaluated using archaeological data. These results suggest that the earliest Australian ancestors adopted a set of fundamental rules shaped by physiological capacity, attraction to visually prominent landscape features and freshwater distribution to maximize survival, even without previous experience of the landscapes they encountered.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

CLINICAL DECISION SUPPORT: PATH TO FUNCTIONAL REQUIREMENTS

Long-duration, deep-space exploration missions present significant challenges to crew health and performance. These challenges include the individual and combined effects of microgravity, radiation exposure, isolation, limited resources (mass, volume, power, data and crew time), limited options for evacuation and those associated with delayed or constrained communications, all of which demand greater crew autonomy. Specifically, as the communication delays intensify the further we explore space, the unqualified need for Earth-independent medical operations focused on autonomous diagnosis, treatment and prevention will be key to mission continuation and success. To augment the requisite knowledge, skills and abilities (KSAs) of a time-constrained crew operating under stressful conditions, combatting fatigue, and facing a potential medical crisis, a robust clinical decision support system (CDSS) is a probable solution that would facilitate, guide and inform Earth-independent medical operations, while assisting crewmembers through various clinical presentations. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) is expanding the boundaries of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit. ExMC is actively identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses gap Medical-701 within the Inflight Medical Conditions risk: “Enhance medical capabilities within an exploration medical system.” Though mass, volume, and power will face increasing constraints, the projected computational capabilities of spacecraft systems will increase exponentially as information technology continues to advance this decade and beyond. Hence, data, software and computational resources will play an essential and synergistic role in maintaining crew health, wellness and performance in deep space missions. The focus of the CDS project is to develop recommended requirements for an in-vehicle CDSS that acts as a ‘virtual assistant’ for delivering optimal health, performance and medical care during exploration missions. The CDSS is envisioned as an integrated, software-based tool deployed on a laptop computer or handheld device. The CDSS will assist the crew and ground support when interacting with knowledge/databases (e.g. records, pharmacy, schedule), instrumentation (e.g. imaging, physiological monitoring devices), and habitat (e.g. wellness system, task performance system) and vehicle systems (e.g. environmental system, communication system). In addition, the human interface will employ a context-based approach that accounts for the crew’s situation. Thus, extraneous and clinically/operationally non-relevant information are reduced to avoid an increase in cognitive load. The framework of an ideal spaceflight CDSS is to include core and advanced analytical features that incorporate work from collaborators yet maintain a flexible platform for integrating new technology in the future. In fiscal year 2021 (FY21), the CDS project identified requirements through two primary mechanisms: (i) the development of software implementation prototypes and (ii) the application of systems engineering processes. The CDS project developed and tested a series of increasingly complex system prototypes that were based on use cases derived from the CDSS concept of operations (ConOps). These software implementations yielded insights on CDSS functionality as well as lessons learned that provided the initial requirements for CDSS capability. By applying a systems engineering (SE) approach, medical scenarios provided in the ConOps and the use cases for software implementation underwent functional decomposition to identify CDSS functionality. Also, systems-based modeling language (SysML) tools such as activity diagrams were developed from the same ConOps and use cases to identify CDSS functionality. The lessons learned from software implementation defined both specific requirements and broad areas of requirements. Within these defined broad requirement areas, further analysis of the SE products identified specific capability that resulted in the final functional requirements. In summary, the software prototypes, functional decomposition of the ConOps and use cases, and SysML diagrams provided the basis for the CDSS requirements developed in FY21. In the upcoming year, these requirements will be refined for their final ExMC baseline review in latter FY22.

clinical decision support↗

NASA NTRSDate not supplied

Clinical Decision Support: Path to Functional Requirements

Long-duration, deep-space exploration missions present significant challenges to crew health and performance. These challenges include the individual and combined effects of microgravity, radiation exposure, isolation, limited resources (mass, volume, power, data and crew time), limited options for evacuation and those associated with delayed or constrained communications, all of which demand greater crew autonomy. Specifically, as the communication delays intensify the further we explore space, the unqualified need for Earth-independent medical operations focused on autonomous diagnosis, treatment and prevention will be key to mission continuation and success. To augment the requisite knowledge, skills and abilities (KSAs) of a time-constrained crew operating under stressful conditions, combatting fatigue, and facing a potential medical crisis, a robust clinical decision support system (CDSS) is a probable solution that would facilitate, guide and inform Earth-independent medical operations, while assisting crewmembers through various clinical presentations. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) is expanding the boundaries of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit. ExMC is actively identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses gap Medical-701 within the Inflight Medical Conditions risk: “Enhance medical capabilities within an exploration medical system.” Though mass, volume, and power will face increasing constraints, the projected computational capabilities of spacecraft systems will increase exponentially as information technology continues to advance this decade and beyond. Hence, data, software and computational resources will play an essential and synergistic role in maintaining crew health, wellness and performance in deep space missions. The focus of the CDS project is to develop recommended requirements for an in-vehicle CDSS that acts as a ‘virtual assistant’ for delivering optimal health, performance and medical care during exploration missions. The CDSS is envisioned as an integrated, software-based tool deployed on a laptop computer or handheld device. The CDSS will assist the crew and ground support when interacting with knowledge/databases (e.g. records, pharmacy, schedule), instrumentation (e.g. imaging, physiological monitoring devices), and habitat (e.g. wellness system, task performance system) and vehicle systems (e.g. environmental system, communication system). In addition, the human interface will employ a context-based approach that accounts for the crew’s situation. Thus, extraneous and clinically/operationally non-relevant information are reduced to avoid an increase in cognitive load. The framework of an ideal spaceflight CDSS is to include core and advanced analytical features that incorporate work from collaborators yet maintain a flexible platform for integrating new technology in the future. In fiscal year 2021 (FY21), the CDS project identified requirements through two primary mechanisms: (i) the development of software implementation prototypes and (ii) the application of systems engineering processes. The CDS project developed and tested a series of increasingly complex system prototypes that were based on use cases derived from the CDSS concept of operations (ConOps). These software implementations yielded insights on CDSS functionality as well as lessons learned that provided the initial requirements for CDSS capability. By applying a systems engineering (SE) approach, medical scenarios provided in the ConOps and the use cases for software implementation underwent functional decomposition to identify CDSS functionality. Also, systems-based modeling language (SysML) tools such as activity diagrams were developed from the same ConOps and use cases to identify CDSS functionality. The lessons learned from software implementation defined both specific requirements and broad areas of requirements. Within these defined broad requirement areas, further analysis of the SE products identified specific capability that resulted in the final functional requirements. In summary, the software prototypes, functional decomposition of the ConOps and use cases, and SysML diagrams provided the basis for the CDSS requirements developed in FY21. In the upcoming year, these requirements will be refined for their final ExMC baseline review in latter FY22.

Clinical decision support↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗

DOE OSTI2025

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

In materials science, plant biology, agriculture, and environmental research, the automated analysis of high-magnification, complex microscopy images, such as those generated by freeze-fracture electron microscopy (FF-TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high-throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF-TEM images. The pipeline integrates preprocessing, detection, human-in-the-loop verification, and semantic segmentation to quantify features such as rosette diameter and inter-lobe spacing. The approach was trained and tested on a curated dataset of high-resolution FF-TEM micrographs of Physcomitrium patens, expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50-95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high-throughput applications. Our results show that the pipeline achieves human-expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)-assisted workflows in biological imaging.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Remotely Administered Psychoacoustic Test for sUAS Noise to Gauge Feasibility of Remote UAM Noise Study

The National Aeronautics and Space Administration (NASA) remotely administered a psychoacoustic test in fall of 2022 as the first of two phases of a cooperative Urban Air Mobility (UAM) vehicle noise human response study. The first phase, the Feasibility Test, described in this paper, determined the feasibility of the remote test method, in contrast to a previous in-person psychoacoustic test that found an annoyance response difference between small unmanned aerial system (sUAS) noise and ground vehicle noise. This paper discusses the Feasibility Test online layout, sound calibration method, remote test software development, stimuli selection, test subject recruitment, and test administration. Test performance is measured through comparison of annoyance response data with the previous in-person test results. The paper also investigates if providing a contextual cue to test subjects influenced their annoyance response. Response differences between test subjects in geographically distinct areas are analyzed. Administrative challenges that were encountered during the test are discussed. The second phase of this study, the implementation phase, will use a remote (web-based) test method and leverage the cooperation of multiple government agencies, academia, and industry to gain human response insights from a wide range of UAM vehicle sounds that would be challenging for a single organization to acquire. Improvements to administering a remote test for the implementation phase of the UAM vehicle noise human response study are recommended.

Remote Psychoacoustic Test↗

NASA NTRSDate not supplied

Remotely Administered Psychoacoustic Test for sUAS Noise to Gauge Feasibility of Remote UAM Noise Study

The National Aeronautics and Space Administration (NASA) remotely administered a psychoacoustic test in fall of 2022 as the first of two phases of a cooperative Urban Air Mobility (UAM) vehicle noise human response study. The first phase, the Feasibility Test, described in this paper, determined the feasibility of the remote test method, in contrast to a previous in-person psychoacoustic test that found an annoyance response difference between small unmanned aerial system (sUAS) noise and ground vehicle noise. This paper discusses the Feasibility Test online layout, sound calibration method, remote test software development, stimuli selection, test subject recruitment, and test administration. Test performance is measured through comparison of annoyance response data with the previous in-person test results. The paper also investigates if providing a contextual cue to test subjects influenced their annoyance response. Response differences between test subjects in geographically distinct areas are analyzed. Administrative challenges that were encountered during the test are discussed. The second phase of this study, the implementation phase, will use a remote (web-based) test method and leverage the cooperation of multiple government agencies, academia, and industry to gain human response insights from a wide range of UAM vehicle sounds that would be challenging for a single organization to acquire. Improvements to administering a remote test for the implementation phase of the UAM vehicle noise human response study are recommended.

Remote Psychoacoustic Test↗

DOE OSTI2020

Deep Reinforcement Learning for Residential HVAC Control with Consideration of Human Occupancy

The Artificial Intelligence (AI) development described herein uses model-free Deep Reinforcement Learning (DRL) to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. Building cooling loads and HVAC operation are difficult to accurately model due to complexity, lack of measurements and data, and model specific performance, so online machine learning is used to allow for real-time readjustment in performance. Energy costs for the multi-zone cooling unit shown in this work are minimized by scheduling on/off commands around dynamic prices. By taking advantage of precooling events that take place when the price is low, the agent is able to reduce operational cost without violating user comfort. The DRL controller was tested in simulation where the learner achieved a 43.89% cost reduction when compared to traditional, fixed-setpoint operation. The system is now ready for the next phase of testing in a live, real-time home environment.

Mckee, Evan↗

NASA NTRS1995

Effects of Space Missions on the Human Immune System: A Meta-Analysis

Future spaceflight will require travelers to spend ever-increasing periods of time in microgravity. Optimal functioning of the immune system is of paramount importance for the health and performance of these travelers. A meta-analysis statistical procedure was used to analyze immune system data from crew members in United States and Soviet space missions from 8.5 to 140 days duration between 1968 and 1985. Ten immunological parameters (immunoglobulins A, G, M, D, white blood cell (WBC) count, number of lymphocytes, percent total lymphocytes, percent B lymphocytes, percent T lymphocytes, and lymphocyte reactivity to mitogen) were investigated using multifactorial, repeated measure analysis of variance. With the preflight level set at 100, WBC count increased to 154 +/- 14% (mean +/- SE; p less than or equal to 0.05) immediately after flight; there was a decrease in lymphocyte count (83 +/- 4%; p less than or equal to 0.05) and percent of total lymphocytes (69 +/- 1%; p less than or equal to 0.05) immediately after flight, with reduction in RNA synthesis to phytohemagglutinin (PHA) to 51 +/- 21% (p less than or equal to 0.05) and DNA synthesis to PHA to 61 +/- 8% (p less than or equal to 0.05) at the first postflight measurement. Thus, some cellular immunological functions are decreased significantly following spaceflight. More data are needed on astronauts' age, aerobic power output, and parameters of their exercise training program to determine if these immune system responses are due solely to microgravity exposure or perhaps to some other aspect of spaceflight.

Greenleaf, J. E.↗

NASA NTRS2021

Collaborative Communications Between A Human and A Resilient Safety Support System

Successful introductory UAM integration into the NAS will be contingent on resilient safety systems that support reduced-crew flight operations. In this paper, we present a system that performs three functions: 1) monitors an operator’s physiological state; 2) assesses when the operator is experiencing anomalous states; and 3) mitigates risks by a combination of dynamic, context-based unilateral or collaborative dynamic function allocation of operational tasks. The monitoring process receives high data-rate sensor values from eye-tracking and electrocardiogram sensors. The assessment process takes these values and performs a classification that was developed using machine learning algorithms. The mitigation process invokes a collaboration protocol called DFACCto which, based on context, performs vehicle operations that the operator would otherwise routinely execute. This system has been demonstrated in a UAM flight simulator for an operator incapacitation scenario. The methods and initial results as well as relevant UAM and AAM scenarios will be described.

Advanced air mobility↗

DOE OSTI2019

Electrochemical Characterization of Certain Mg-Based Alloys in Artificial Perspiration Biofluid for Consumer and Industrial Applications

The weight reduction of numerous consumer electronic devices is of increasing importance. In this respect, ultra-light magnesium alloys have attracted more attention to be used as frame materials. Nevertheless, Mg alloys are of intrinsically poor corrosion resistance, especially in chloride ion-containing environments. Human perspiration fluid with a relatively high chloride ions content (~ 0.14 M) comes in contact with a number of consumer products resulting in a variety of undesirable effects such as malfunction and corrosion. Based on that, the present work addresses the corrosion behavior of five AZ-Mg alloys including AZ91D, AZ80E, AZ31, AM60 and AXJ530 in artificial perspiration biofluid at human body temperature of 37 °C. The study is performed using electrochemical impedance spectroscopy and potentiodynamic polarization techniques supported by surface morphological and EDX spectra examinations. The surface layer total resistance value (R{sub T}) estimated from the analysis of obtained impedance data after 12-h exposure period reveals that durability of our tested samples increases in the following sequence: AZ80 > AZ91 > AZ31 > AM60 > AXJ530 > Mg (control). AZ80E alloy at top of this sequence points to its significantly high anti-corrosion performance in artificial perspiration biofluid and thus can be the best Mg materials of choice for many housing consumer electronic products. This outcome is in consistence with the corrosion rate (P{sub i} in mm y{sup −1}) trend as derived from the potentiodynamic polarization data that decreases in the following order: AZ80 (0.25) < AZ91 (0.49) < AZ31 (1.03) < AM60 (1.23) < AXJ530 (5.36) < Mg (5.98). Both FE-SEM images and EDX analyses further confirm these experimentally electrochemical findings.

36 MATERIALS SCIENCE↗

DOE OSTI2014

SU-E-I-30: Image Analysis in Ultrasonography for Diagnosis of Sjoegren's Syndrome Using Dual-Tree Complex Wavelet Transform

Purpose: Sjoegren's syndrome (SS) is an autoimmune disease invading mainly salivary and lacrimal glands. Ultrasonography is used for an initial and non-invasive examination of this disease. However, the ultrasonography diagnosis tends to lack in objectivity and depends on the operator's skills. The purpose of this study is to propose a computer-aided diagnosis (CAD) system for SS based on a dual-tree complex wavelet transform (DT-CWT) and machine learning. Methods: The subjects of this study were 174 patients suspected of having SS at Nagasaki University Hospital and examined with ultrasonography of the parotid glands. Out of these patients, 77 patients were diagnosed with SS by sialography. A region of interest (ROI) of 128 × 128 pixels was set within the parotid gland that was indicated by a dental radiologist. The DT-CWT was applied to the images in the ROI and every image was decomposed into 72 sub-images of the real and imaginary components in six different resolution levels and six orientations. The statistical features of the sub-image were calculated and used as data input for the support vector machine (SVM) classifier for the detection of SS. A ten-fold cross-validation was employed to verify the Resultof SVM. The accuracy of diagnosis was compared by a CAD system with a human observer performance. Results: The sensitivity, specificity, and accuracy in the detection of SS were 95%, 86%, and 91% through our CAD system respectively, while those by a human observer were 84%, 81%, and 83% respectively. Conclusion: The proposed computer-aided diagnosis system for Sjoegren's syndrome in ultrasonography based on dual-tree complex wavelet transform had a better performance than a human observer.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

An ecological niche model to predict the geographic distribution of Haemagogus janthinomys, Dyar, 1921 a yellow fever and Mayaro virus vector, in South America

Yellow fever virus (YFV) has a long history of impacting human health in South America. Mayaro virus (MAYV) is an emerging arbovirus of public health concern in the Neotropics and its full impact is yet unknown. Both YFV and MAYV are primarily maintained via a sylvatic transmission cycle but can be opportunistically transmitted to humans by the bites of infected forest dwelling Haemagogus janthinomys Dyar, 1921. To better understand the potential risk of YFV and MAYV transmission to humans, a more detailed understanding of this vector species’ distribution is critical. This study compiled a comprehensive database of 177 unique Hg . janthinomys collection sites retrieved from the published literature, digitized museum specimens and publicly accessible mosquito surveillance data. Covariate analysis was performed to optimize a selection of environmental (topographic and bioclimatic) variables associated with predicting habitat suitability, and species distributions modelled across South America using a maximum entropy (MaxEnt) approach. Our results indicate that suitable habitat for Hg . janthinomys can be found across forested regions of South America including the Atlantic forests and interior Amazon.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1976

A method for generating numerical pilot opinion ratings using the optimal pilot model

A method for generating numerical pilot opinion ratings using the optimal pilot model is introduced. The method is contained in a rating hypothesis which states that the numerical rating which a human pilot assigns to a specific vehicle and task can be directly related to the numerical value of the index of performance resulting from the optimal pilot modeling procedure as applied to that vehicle and task. The hypothesis is tested using the data from four piloted simulations. The results indicate that the hypothesis is reasonable, but that the predictive capability of the method is a strong function of the accuracy of the pilot model itself. This accuracy is, in turn, dependent upon the parameters which define the optimal modeling problem. A procedure for specifying the parameters for the optimal pilot model in the absence of experimental data is suggested.

Hess, R. A.↗

NASA NTRS2000

Modeling a Wireless Network for International Space Station

This paper describes the application of wireless local area network (LAN) simulation modeling methods to the hybrid LAN architecture designed for supporting crew-computing tools aboard the International Space Station (ISS). These crew-computing tools, such as wearable computers and portable advisory systems, will provide crew members with real-time vehicle and payload status information and access to digital technical and scientific libraries, significantly enhancing human capabilities in space. A wireless network, therefore, will provide wearable computer and remote instruments with the high performance computational power needed by next-generation 'intelligent' software applications. Wireless network performance in such simulated environments is characterized by the sustainable throughput of data under different traffic conditions. This data will be used to help plan the addition of more access points supporting new modules and more nodes for increased network capacity as the ISS grows.

Alena, Richard↗