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Subsonic Aircraft With Regression and Neural-Network Approximators Designed
At the NASA Glenn Research Center, NASA Langley Research Center's Flight Optimization System (FLOPS) and the design optimization testbed COMETBOARDS with regression and neural-network-analysis approximators have been coupled to obtain a preliminary aircraft design methodology. For a subsonic aircraft, the optimal design, that is the airframe-engine combination, is obtained by the simulation. The aircraft is powered by two high-bypass-ratio engines with a nominal thrust of about 35,000 lbf. It is to carry 150 passengers at a cruise speed of Mach 0.8 over a range of 3000 n mi and to operate on a 6000-ft runway. The aircraft design utilized a neural network and a regression-approximations-based analysis tool, along with a multioptimizer cascade algorithm that uses sequential linear programming, sequential quadratic programming, the method of feasible directions, and then sequential quadratic programming again. Optimal aircraft weight versus the number of design iterations is shown. The central processing unit (CPU) time to solution is given. It is shown that the regression-method-based analyzer exhibited a smoother convergence pattern than the FLOPS code. The optimum weight obtained by the approximation technique and the FLOPS code differed by 1.3 percent. Prediction by the approximation technique exhibited no error for the aircraft wing area and turbine entry temperature, whereas it was within 2 percent for most other parameters. Cascade strategy was required by FLOPS as well as the approximators. The regression method had a tendency to hug the data points, whereas the neural network exhibited a propensity to follow a mean path. The performance of the neural network and regression methods was considered adequate. It was at about the same level for small, standard, and large models with redundancy ratios (defined as the number of input-output pairs to the number of unknown coefficients) of 14, 28, and 57, respectively. In an SGI octane workstation (Silicon Graphics, Inc., Mountainview, CA), the regression training required a fraction of a CPU second, whereas neural network training was between 1 and 9 min, as given. For a single analysis cycle, the 3-sec CPU time required by the FLOPS code was reduced to milliseconds by the approximators. For design calculations, the time with the FLOPS code was 34 min. It was reduced to 2 sec with the regression method and to 4 min by the neural network technique. The performance of the regression and neural network methods was found to be satisfactory for the analysis and design optimization of the subsonic aircraft.
Education, training, and human engineering in aerospace; SAE Aerotech '93, Costa Mesa, CA, Sep. 27-30, 1993
Advances in simulation technology are discussed by a number of government and industry experts, for both training and research and development applications. Advanced techniques, such as helmet-mounted information displays, neurocontrollers, automated training systems, and simulation for space-based systems are included. Advances in training methodology for air transportation are covered by a group of experts in that field, including discussions of advanced flight deck transition training, new training tools, and effective low cost alternatives for part-task training. With the ever-increasing emphasis on human factors in cockpit and cabin design, the section on research, advances, and certification criteria in that field is pertinent. NASA, aircraft manufacturing, and FAA representatives have compiled an informative group of presentations concerning active topics and considerations in human factors design.
Skeletal muscle Ca(2+)-independent kinase activity increases during either hypertrophy or running
Spikes in free Ca(2+) initiate contractions in skeletal muscle cells, but whether and how they might signal to transcription factors in skeletal muscles of living animals is unknown. Since previous studies in non-muscle cells have shown that serum response factor (SRF) protein, a transcription factor, is phosphorylated rapidly by Ca(2+)/calmodulin (CaM)-dependent protein kinase after rises in intracellular Ca(2+), we measured enzymatic activity that phosphorylates SRF (designated SRF kinase activity). Homogenates from 7-day-hypertrophied anterior latissimus dorsi muscles of roosters had more Ca(2+)-independent SRF kinase activity than their respective control muscles. However, no differences were noted in Ca(2+)/CaM-dependent SRF kinase activity between control and trained muscles. To determine whether the Ca(2+)-independent and Ca(2+)/CaM-dependent forms of Ca(2+)/CaM-dependent protein kinase II (CaMKII) might contribute to some of the SRF kinase activity, autocamtide-3, a synthetic substrate that is specific for CaMKII, was employed. While the Ca(2+)-independent form of CaMKII was increased, like the Ca(2+)-independent form of SRF kinase, no alteration in CaMKII occurred at 7 days of stretch overload. These observations suggest that some of SRF phosphorylation by skeletal muscle extracts could be due to CaMKII. To determine whether this adaptation was specific to the exercise type (i.e., hypertrophy), similar measurements were made in the white vastus lateralis muscle of rats that had completed 2 wk of voluntary running. Although Ca(2+)-independent SRF kinase was increased, no alteration occurred in Ca(2+)/CaM-dependent SRF kinase activity. Thus any role of Ca(2+)-independent SRF kinase signaling has downstream modulators specific to the exercise phenotype.
(abstract) Mission Operations and Control Assurance: Flight Operations Quality Improvements
Mission Operations and Command Assurance (MO&CA), a recent addition to flight operations teams at JPL. provides a system level function to instill quality in mission operations. MO&CA's primary goal at JPL is to help improve the operational reliability for projects during flight. MO&CA tasks include early detection and correction of process design and procedural deficiencies within projects. Early detection and correction are essential during development of operational procedures and training of operational teams. MO&CA's effort focuses directly on reducing the probability of radiating incorrect commands to a spacecraft. Over the last seven years at JPL, MO&CA has become a valuable asset to JPL flight projects. JPL flight projects have benefited significantly from MO&CA's efforts to contain risk and prevent rather than rework errors. MO&CA's ability to provide direct transfer of knowledge allows new projects to benefit directly from previous and ongoing experience. Since MO&CA, like Total Quality Management (TQM), focuses on continuous improvement of processes and elimination of rework, we recommend that this effort be continued on NASA flight projects.
Electrophysiology of the mammillary complex in vitro. I. Tuberomammillary and lateral mammillary neurons
1. The electrophysiological properties of the tuberomammillary and lateral mammillary neurons in the guinea pig mammillary body were studied using an in vitro brain slice preparation. 2. Tuberomammillary (n = 79) neurons were recorded mainly ventral to the lateral mammillary body as well as ventromedially to the fornix within the rostral part of the medial mammillary nucleus. Intracellular staining with horseradish peroxidase (n = 9) and Lucifer yellow (n = 3) revealed that these cells have several thick, long, spiny dendrites emerging from large (20-35 microns) fusiform somata. 3. Most tuberomammillary neurons (66%) fired spontaneously at a relatively low frequency (0.5-10 Hz) at the resting membrane potential. The action potentials were broad (2.3 ms) with a prominent Ca(2+)-dependent shoulder on the falling phase. Deep (17.8 mV), long-lasting spike afterhyperpolarizations were largely Ca(2+)-independent. 4. All tuberomammillary neurons recorded displayed pronounced delayed firing when the cells were activated from a potential negative to the resting level. The cells also displayed a delayed return to the baseline at the break of hyperpolarizing pulses applied from a membrane potential level close to firing threshold. Analysis of the voltage- and time dependence of this delayed rectification suggested the presence of a transient outward current similar to the A current (IA). These were not completely blocked by high concentrations of 4-aminopyridine, whereas the delayed onset of firing was always abolished when voltage-dependent Ca2+ conductances were blocked by superfusion with Cd2+. 5. Tuberomammillary neurons also displayed inward rectification in the hyperpolarizing and, primarily, depolarizing range. Block of voltage-gated Na(+)-dependent conductances with tetrodotoxin (TTX) selectively abolished inward rectification in the depolarizing range, indicating the presence of a persistent low-threshold sodium-dependent conductance (gNap). In fact, persistent TTX-sensitive, plateau potentials were always elicited following Ca2+ block with Cd2+ when K+ currents were reduced by superfusion with tetraethylammonium. 6. The gNap in tuberomammillary neurons may subserve the pacemaker current underlying the spontaneous firing of these cells. The large-amplitude spike afterhyperpolarization of these neurons sets the availability of the transient outward rectifier, which, in conjunction with the pacemaker current, establishes the rate at which membrane potential approaches spike threshold. 7. Repetitive firing elicited by direct depolarization enhanced the spike shoulder of tuberomammillary neurons. Spike trains were followed by a Ca(2+)-dependent, apamine-sensitive, slow afterhyperpolarization. 8. Lateral mammillary neurons were morphologically and electrophysiologically different from tuberomammillary neurons. All lateral mammillary neurons neurons recorded (n = 44) were silent at rest (-60 mV).(ABSTRACT TRUNCATED AT 400 WORDS).
NASA-Navy Telemedicine: Autogenic Feedback Training Exercises for Motion Sickness
Airsickness is the most significant medical condition affecting naval aviation training. A 2001 study showed that airsickness was reported in 81% of naval aviation students and was associated with 82% of below average flight scores. The cost to a single training air-wing was over $150,000 annually for fuel and maintenance costs alone. Resistent cases are sent to the Naval Aerospace Medical Institute (NAMI) for evaluation and desensitization in the self-paced airsickness desensitization (SPAD) program. This approach is 75% successful, but can take up to 8 weeks at a significant travel cost. NASA Ames Research Center's Autogenic Feedback Training Exercises (AFTE) uses physiological and biofeedback training for motion sickness prevention. It has a remote capability that has been used from Moffett Field, CA to Atlanta, GA . AFTE is administered in twelve (30-minute) training sessions. The success rate for the NASA AFTE program has been over 85%. Methods: Implementation Phases: Phase I: Transfer NASA AFTE to NAMI; NASA will remotely train aviation students at NAMI. Phase II: NAMI-centered AFTE application with NASA oversight. Phase III: NAMI-centered AFTE to remotely train at various Navy sites. Phase IV: NAMI to offer Tri-service application and examine research opportunities. Results: 1. Use available telemedicine connectivity between NAMI and NASA. 2. Save over $2,000 per student trained. 3. Reduce aviation training attrition. 4. Provide standardization of multi-location motion sickness training. 5. Future tri-service initiatives. 6. Data to NASA and Navy for QA and research opportunities.
Effect of swim exercise training on human muscle fiber function
The effect of swim exercise training on the human muscle fiber function was investigated in swimmers trained in a typical collegiate swim-training program followed by an intensified 10-day training period. The measured parameters included the peak tension (P0), negative log molar Ca(2+) concentration (pCa)-force, and maximal shortening speed (Vmax) of the slow-twitch type I and fast-twitch type II fibers obtained by biopsy from the deltoid muscle. The P0 values were found to be not altered after either the training or the 10-day intensive program. The type I fibers from the trained swimmers showed pCa-force curves shifted to the right, such that higher free Ca(2+) levels were required to elicit a given percent of P0. The training program significantly increased the Vmax in the type I fibers and decreased that of the type II fibers, and the 10-day intensive training produced a further significant decrease of the type II fibers.
The Development of Real-Time Physiological Monitoring and Training Software for Remote Applications
Autogenic Feedback Training Exercise (AFTE) is an protocol and technology developed by Dr. Patricia Cowings and her associates at NASA Ames Research Center as a means to facilitate astronaut adaptation to space and exposure to the microgravity. AFTE is a training method which involves teaching subjects to voluntarily control several of their own physiological responses to environmental stressors. As the procedures matured, the training program was expanded to determine if technology developed to facilitate astronaut adaptation to space would be valuable in treating patients suffering from autonomic and vestibular pathologies and symptomatic relief from nausea and/or blood pressure control anomalies such as hypo- or hypertension. The present study, performed in conjunction with Morehouse School of Medicine, Biomedical Engineering at The University of Akron and NASA Ames Research Center has demonstrated that this technology can be successfully applied over vast distances. The specific purpose of this research was to develop a PC based system which could handle processing of twenty channels of acquired physiological data in addition to the necessary duplex communication protocols that would, for example, permit a patient in Atlanta, GA to be trained by a clinician stationed in San Jose, CA. Sixteen channels of physiological data and 20 channels of processed data are included.
Refinement of a Method for Identifying Probable Archaeological Sites from Remotely Sensed Data
To facilitate locating archaeological sites before they are compromised or destroyed, we are developing approaches for generating maps of probable archaeological sites, through detecting subtle anomalies in vegetative cover, soil chemistry, and soil moisture by analyzing remotely sensed data from multiple sources. We previously reported some success in this effort with a statistical analysis of slope, radar, and Ikonos data (including tasseled cap and NDVI transforms) with Student's t-test. We report here on new developments in our work, performing an analysis of 8-band multispectral Worldview-2 data. The Worldview-2 analysis begins by computing medians and median absolute deviations for the pixels in various annuli around each site of interest on the 28 band difference ratios. We then use principle components analysis followed by linear discriminant analysis to train a classifier which assigns a posterior probability that a location is an archaeological site. We tested the procedure using leave-one-out cross validation with a second leave-one-out step to choose parameters on a 9,859x23,000 subset of the WorldView-2 data over the western portion of Ft. Irwin, CA, USA. We used 100 known non-sites and trained one classifier for lithic sites (n=33) and one classifier for habitation sites (n=16). We then analyzed convex combinations of scores from the Archaeological Predictive Model (APM) and our scores. We found that that the combined scores had a higher area under the ROC curve than either individual method, indicating that including WorldView-2 data in analysis improved the predictive power of the provided APM.
Bayes classification of interferometric TOPSAR data
We report the Bayes classification of terrain types at different sites using airborne interferometric synthetic aperture radar (INSAR) data. A Gaussian maximum likelihood classifier was applied on multidimensional observations derived from the SAR intensity, the terrain elevation model, and the magnitude of the interferometric correlation. Training sets for forested, urban, agricultural, or bare areas were obtained either by selecting samples with known ground truth, or by k-means clustering of random sets of samples uniformly distributed across all sites, and subsequent assignments of these clusters using ground truth. The accuracy of the classifier was used to optimize the discriminating efficiency of the set of features that was chosen. The most important features include the SAR intensity, a canopy penetration depth model, and the terrain slope. We demonstrate the classifier's performance across sites using a unique set of training classes for the four main terrain categories. The scenes examined include San Francisco (CA) (predominantly urban and water), Mount Adams (WA) (forested with clear cuts), Pasadena (CA) (urban with mountains), and Antioch Hills (CA) (water, swamps, fields). Issues related to the effects of image calibration and the robustness of the classification to calibration errors are explored. The relative performance of single polarization Interferometric data classification is contrasted against classification schemes based on polarimetric SAR data.
AC and DC Fault Management for Megawatt Electrified Aircraft Electrical Powertrains - Task 2: Power Quality Filtering Using Nanocrystalline Soft Magnetic Inductor
The NASA RTAPS program on AC and DC Fault Management for Megawatt Electrified Power Train is a multi-year joint project with Pratt & Whitney (P&W), Collins Aerospace (CA), and RTX Technology Research Center (RTRC). This research program focuses on the high-voltage distribution issues that present a significant technological obstacle in the adoption of Electrified Aviation Propulsion (EAP) systems. One challenge to the adoption of high-voltage distribution systems with power electronic converters is the need for filter elements to limit the generation and propagation of noise, protect the cable systems from premature aging and prevent against excessive heating within subcomponents due to high-frequency induced currents. While increased distribution voltages aide in reducing the cable mass for a fixed power system, the associated mass with the filtering elements for power electronic converter can grow with increasing distribution voltages – thereby mitigating any benefit associated with increasing the distribution system voltage. To enable high-voltage distribution systems with high system specific power densities, new magnetic materials must be developed. Therefore, the second task of the NASA RTAPS program is associated with the design and application of advanced soft magnetic components for Megawatt class electric propulsion systems, specifically the motor drive system. This report covers the collaborative work between NASA Glenn Research Center (GRC), RTRC, P&W and CA in the development of three types of magnetic components over the span of the three-year program. These critical magnetic components are the DC side EMI filter, which limits the propagation of harmful electromagnetic noise to the rise of the distribution system, and the AC side damping with the dv/dt filter, which limits the fast rise time of the power electronic converter output voltage to limit the degradation on the cable/motor insulation systems. Each of these components are investigated from component level design and are optimized at the system level with a combined modelling and testing effort. In the final experimental evaluation of the NASA developed soft magnetic material with a dv/dt filter, a commercial-off-the-shelf (COTS) magnetic core and the GRC magnetic core are optimized and loaded at 320Arms to evaluate their difference in performance. After a run time of 30 minutes in a MW-class motor driver at RTRC, the NASA GRC cores were found to not only offer a lower temperature rise of nearly 25°𝐶, but also a reduction in measured core loss of 25% (12.75W to 9.5W).
Effects of moderate heart failure and functional overload on rat plantaris muscle
It is thought that changes in sarco(endo)plasmic reticulum Ca(2+)-ATPase (SERCA) of skeletal muscle contribute to alterations in skeletal muscle function during congestive heart failure (CHF). It is well established that exercise training can improve muscle function. However, it is unclear whether similar adaptations will result from exercise training in a CHF patient. Therefore, the purpose of this study was to determine whether skeletal muscle during moderate CHF adapts to increased activity, utilizing the functional overload (FO) model. Significant increases in plantaris mass of the CHF-FO and sham-FO groups compared with the CHF and control (sham) groups were observed. Ca(2+) uptake rates were significantly elevated in the CHF group compared with all other groups. No differences were detected in Ca(2+) uptake rates between the CHF-FO, sham, and sham-FO groups. Increases in Ca(2+) uptake rates in moderate-CHF rats were not due to changes in SERCA isoform proportions; however, FO may have attenuated the CHF-induced increases through alterations in SERCA isoform expression. Therefore, changes in skeletal muscle Ca(2+) handling during moderate CHF may be due to alterations in regulatory mechanisms, which exercise may override, by possibly altering SERCA isoform expression.
Development of Machine Learning Algorithms to Segment and Study Images of Astromaterial Samples
Introduction: Micrometer-scale chemical analyses of chondritic meteorites and mission-returned asteroid samples can reveal details of the physical and chemical processes operating in the early solar system, including processes that gave rise to planets, moons, and minor bodies. These primitive astromaterials are comprised of chondrules, calcium- and aluminum-rich inclusions (CAI), and many other silicates, oxides, metals, sulfides, and fine-grained materials. The chemical and mineralogical complexity of these samples, vast populations of different components, and heterogeneity across mm to km scales, all limit our understanding of the origin and evolution of these materials. Here, we describe recent efforts to use machine learning techniques to automate the segmentation of chemical maps of chondritic meteorites, designed to aid studies of asteroid samples returned by spacecraft. By automating the task of segmentation it will become possible to rapidly analyze and interpret the sizes, shapes, mineralogy, chemistry, and other properties of every chondrule, calcium- and aluminum-rich inclusion (CAI) and other clast within and between asteroid samples. Sample return missions significantly accelerate and heighten the need to develop such new data analysis techniques, and associated data repositories. Techniques: Neural networks require abundant training data, i.e. images which have been segmented by a human user. We have manually segmented data available from previous petrologic and chemical work at NASA Johnson Space Center and the American Museum of Natural History [1-4]. These data were derived from energy- and wavelength-dispersive X-ray spectroscopy (EDS, WDS) mapping of samples from many chondrite groups. The Deeplabv3+ [5] neural network architecture was trained on human-labeled masks and used to create machine-labeled masks. Several different algorithms were investigated, with inputs ranging from common RGB image formats through to hyperspectral datasets, with raw data comprising greyscale maps of Mg, Ca, and Al, with or without Si, Fe, Ti for both EDS and WDS data, and extending to other elements in EDS only. Each greyscale image was paired with a binary mask for each labelled particle type. Results: The trained algorithms can segment (Fig 1), classify, and measure the dimensions of thousands of particles in chemical maps of a standard 1-inch round petrographic section in seconds to minutes, rather than many hours needed by a human. Accuracy of the algorithms varied from chondrite to chondrite and across particle types. Further results and details of the algorithms will be presented at the workshop. Future directions: Machine learning has the potential to revolutionize our understanding of complex particle populations contained within primitive astromaterial, with segmentation being a critical first step. Example applications include better understanding of particle transport, nebular reservoirs, parent body accretion, and a deeper understanding of the relationships between particle populations and bulk rock elemental and isotopic compositions. In addition to benefits that machine learning can bring to individual researchers, building a community data repository of thousands to millions of particles across hundreds of samples will open up many other possibilities. For example, with a large enough dataset it will be possible to search for exceptionally closely matching particles across disparate samples. Such a capability would enable a single CAI from OSIRISREx or Hayabusa/II samples to be matched to chondritic CAIs that exhibit near-identical size, texture, and mineralogy, down to the level of similar core phenocrysts, zonation, and rim sequences. Such comparative analyses will help to disentangle precursor chemistry, chronology, gas/dust reservoirs during heating, and accretion. Such an endeavor would be impossible without machine learning and a large community data repository of astromaterial chemical/mineralogic maps.
Development of a Complete Landsat Evapotranspiration and Energy Balance Archive to Support Agricultural Consumptive Water Use Reporting and Prediction in the Central Valley, CA
Mapping evapotranspiration (ET) from agricultural areas in Californias Central Valley is critical for understanding historical consumptive use of surface and groundwater. In addition, long histories of ET maps provide valuable training information for predictive studies of surface and groundwater demands. During times of drought, groundwater is commonly pumped to supplement reduced surface water supplies in the Central Valley. Due to the lack of extensive groundwater pumping records, mapping consumptive use using satellite imagery is an efficient and robust way for estimating agricultural consumptive use and assessing drought impacts. To this end, we have developed and implemented an algorithm for automated calibration of the METRIC remotely sensed surface energy balance model on NASAs Earth Exchange (NEX) to estimate ET at the field scale. Using automated calibration techniques on the NEX has allowed for the creation of spatially explicit historical ET estimates for the Landsat archive dating from 1984 to the near present. Further, our use of spatial NLDAS and CIMIS weather data, and spatial soil water balance simulations within the NEX METRIC workflow, has helped overcome challenges of time integration between satellite image dates. This historical and near present time archive of agricultural water consumption for the Central Valley will be an extremely useful dataset for water use and drought impact reporting, and predictive analyses of groundwater demands.
Training augmentation device for the Air Force satellite Control Network
From the 1960's and into the early 1980's satellite operations and control were conducted by Air Force Systems Command (AFSC), now Air Force Materiel Command (AFMC), out of the Satellite Control Facility at Onizuka AFB, CA. AFSC was responsible for acquiring satellite command and control systems and conducting routine satellite operations. The daily operations, consisting of satellite health and status contacts and station keeping activities, were performed for AFSC by a Mission Control Team (MCT) staffed by civilian contractors who were responsible for providing their own technically 'qualified' personnel as satellite operators. An MCT consists of five positions: mission planner, ground controller, planner analyst, orbit analyst, and ranger controller. Most of the training consisted of On-the-Job-Training (OJT) with junior personnel apprenticed to senior personnel until they could demonstrate job proficiency. With most of the satellite operators having 15 to 25 years of experience, there was minimal risk to the mission. In the mid 1980's Air Force Space Command (AFSPACOM) assumed operational responsibility for a newly established control node at Falcon AFB (FAFB) in CO. The satellites and ground system program offices (SPO's) are organized under AFSC's Space and Missiles Systems Center (SMC) to function as a systems engineering and acquisition agency for AFSPACECOM. The collection of the satellite control nodes, ground tracking stations, computer processing equipment, and connecting communications links is referred to as the Air Force Satellite Control Network (AFSCN).
Modeling Microgravity Induced Fluid Redistribution Autoregulatory and Hydrostatic Enhancements
Space flight induces a marked cephalad (headward) redistribution of blood and interstitial fluid potentially resulting in a loss of venous tone and reduction in heart muscle efficiency upon introduction into the microgravity environment. Using various types of computational models, we are investigating how this fluid redistribution may induce intracranial pressure changes, relevant to reported reductions in astronaut visual acuity, part of the Visual Impairment and Intracranial Pressure (VIIP) syndrome. Methods: We utilize a lumped parameter cardiovascular system (CVS) model, augmented by compartments comprising the cerebral spinal fluid (CSF) space, as the primary tool to describe how microgravity, and the associated lack of hydrostatic gradient, impacts fluid redistribution. Models of ocular fluid pressures and biomechanics then accept the output of the above model as boundary condition input to allow more detailed, local analysis (see IWS Abstract by Ethier et al.). Recently, we enhanced the capabilities our previously reported CVS model through the implementation of robust autoregulatory mechanisms and a more fundamental approach to the implementation of hydrostatic mechanisms. Modifying the approach of Blanco et al., we implemented auto-regulation in a quasi-static manner, as an averaged effect across the span of one heartbeat. This approach reduced the higher frequency perturbations from the regulatory mechanism and was intended to allow longer simulation times (days) than models that implement within-beat regulatory mechanisms (minutes). A more fundamental approach to hydrostatics was implemented by a quasi-1D approach, in which compartment descriptions include compartment length, orientation and relative position, allowed for modeling of body orientation, relative body positioning and, in the future, alternative gravity environments. At this time the inclusion of hydrostatic mechanisms supplies additional capabilities to train and validate the CVS model with terrestrial data. Results and Conclusions: With the implementation of auto-regulation and hydrostatic modeling capabilities, the model performs as expected in the maintaining the CA (Central Artery) compartment pressure when simulating orientations ranging from supine to standing. The model appears to generally overpredict heart rate and thus cardiac output, possibly indicating sensitivity to the nominal heart rate, which is used as an initial set point of the regulation mechanisms. Despite this sensitivity, the model performs consistently for many hours of simulation time, indicating the success of our quasi-static implementation approach.
Algorithmic Detection of Elemental Biosignatures
Machine learning models that classify a sample as indicative or non-indicative of life could play an important role in life-detection missions. Their predictions result from agnostic algorithms and thereby add redundancy to judgements resulting from human expertise. Additionally, their important features can reveal the most informative measurements within the operational constraints of a life-detection mission. The Ladder of Life Detection (Neveu 2018) identifies the need for an understanding of how combinations of multiple biosignatures affect overall confidence. The present work provides a starting point to answer this need, and future work will expand the data types to obtain even more predictive combinations of features. Elemental abundance was chosen as a starting set of features due to its availability in diverse sample types, which are needed to train a generalizable model. A standardized dataset was collected, including 35 non-indicative, e.g., lunar rock, basalt; 19 indicative mixed, e.g., seawater, agricultural soil; 46 indicative non-alive, e.g., coal, chalk; and 10 indicative alive, e.g., biofilm, bacteria. This dataset could be valuable for complementary biosignature research. The samples were standardized to the same limit of detection of a simulated mission scenario. Four classification models were used: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), and Gaussian naïve Bayes (GNB). To obtain feature importances, KNN was run on three principal components of the training data and LR and SVM were run with L1 and L2 regularization. The performances and feature importances of the six model variants on 40:60 train to validation ratios were assessed with Monte Carlo simulations. ROC AUC and mean accuracy scores ranged between 82% - 94%, with sensitivity greater than specificity. For indicative of life predictors, all models had C and Ca as strong and Cl as medium; a majority of models had N, K, and P as medium. For non-indicative of life predictors, all models had Si as strong, and a majority of models had Mg, Al, and Ti as medium. Varied elements were Fe (slightly non-indicative), H (slightly indicative), O (widely varied), Na, Mn, and S. These results serve as a proof of concept and suggest important elemental signals beyond merely the CHNOPS of Earth-based life.