SEARCH · Engineering Papers

Results for “sequencing test”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,585 records · Page 88

All sources NASA NTRS DOE OSTI

NASA NTRS1992

Experimental investigation of nozzle/plume aerodynamics at hypersonic speeds

An extensive program to improve the operation of the Ames 16 Inch Shock Tunnel was carried out. This included reduction of driver slosh wave amplitudes and detonation risk by the use of premixed He/O2 gas, longer wait times between the last gas load and driver gas ignition, an improved gas loading sequence, the use of four instead of one ignition wire, and the use of lower ignition wire voltages. Successful operation of the tunnel at pressures of 2000-6000 psi and enthalpies up to 12,000 J/gm was achieved. A new diaphragm breaking technique, self break on the driver combustion pressure rise, was tested and found to produce clean breaks over the full pressure range of the tunnel. Improvements were made to the driver gas loading manifold and a preliminary design was made of a gas mixing system which mixes all three gases on the fly just before injection into the driver. Other improvements as well as tests are discussed.

Bogdanoff, David W.↗

NASA NTRS1989

PRIME: A bottom-up approach to probabilistic rule development

PRIME is a system to be used by an intelligent machine to allow it to operate in an abstract but uncertain (or stochastic) environment. It maintains a model of the effects of the machine's actions in the form of a rule base, which is induced from experience. This bottom-up approach to rule development allows the model to adapt to changes in the environment. Each rule consists of a condition under which the rule is active, an action, the effect of the action on the environment, and an estimate of the probability of this effect occurring. The effect probabilities are used to model the uncertainty in the environment, permitting multiple possible effects for a single action under a particular set of conditions. The objective of the intelligent machine is to satisfy user-specified goals with maximum probability of success. PRIME fulfills this requirement in two ways: it continuously updates the rule base with the most recent information, to ensure the validity of the model; and it generates plans which have the maximum probability of achieving the goals, based on the probability estimates in the rule base. PRIME is composed of three main processes: exploration, generalization, and planning. In exploration, the machine executes various randomly chosen actions, observes the effects on the environment, and updates the rule base accordingly. This process is used to develop the rule base in simulation, as well as to supplement the current knowledge during normal operation. Generalization is the procedure used to induce general rules from experience, which is encoded in the form of specific rules. These general rules extend the machine's knowledge to situations which have not been encountered yet, thereby increasing the capability of the machine to plan effectively. Planning is the process of constructing an optimal sequence of actions to satisfy a goal, using the rule base to predict the effects of these actions and to determine the probability of success of the plan. The rule representation and many other data structures were specifically chosen to maximize the efficiency of these processes. A simulated environment was designed to test the performance of PRIME. The results of experimentation were largely negative. The main problem was that the domain coverage of the rules was inadequate for the number of rules stored in the rule base, due to redundancies in general rules and numerous rules covering ineffective actions. It was determined that a more efficient generalization, and some form of goal-directed exploration, are necessary in order to solve most of the current deficiencies in PRIME.

Miller, Scott A.↗

NASA NTRS1995

Sea surface topography fields of the tropical Pacific from data assimilation

Time series of maps of monthly tropical Pacific dynamic topography anomalies from 1979 through 1985 were constructed by means of assimilation of the tide gauge and expendable bathythermograph (XBT) data into a linear model driven by observed winds. Estimates of error statistics were calculated and compared to actual differences between hindcasts and observations. Four experiements were performed as follows: one with no assimilation, one with assimiation of sea level anomaly data from eight selected island tide gauge stations, one with assimilation of dynamic height anomalies derived from XBT data, and one with both XBT and tide gauge data assimilated. Data from seven additional tide gauge stations were withheld from the assimilation process and used for verification in all four experiements. Statistical objective maps based on data alone were also constructed for comparison purposes. The dynamic response of the model without assimilation was, in general, weaker than the observed response. Assimilation resulted in enhanced signal amplitude in all three assimilation experiments. With few exceptions the error estimated generated by the Kalman filter appeared quite reasonable. Since the error processes cannot be assumed to be white or stationary, we could find no straightforward way to test the formal statistical hypothesis that the time series of differences between the filter ouput and the actual observations were drawn from a population with statistics given by the Kalman filter estimaes. We found that the best filter we could devise was still short of the goal of producing a white innovation sequence. Extensive changes in the assumed error statistics make only marginal differences. The same is true for long time and space scale behavior of different models with richer physics and finer resolution. Better data assimilation results will probably require relaxation of the assumptions of stationarity and serial independence of the errors. Formation of such detailed noise models will require longer time series, with the attendant problems of matching very different data sets.

Miller, Robert N.↗

NASA NTRS1996

A Novel Kinesin-Like Protein with a Calmodulin-Binding Domain

Calcium regulates diverse developmental processes in plants through the action of calmodulin. A cDNA expression library from developing anthers of tobacco was screened with S-35-labeled calmodulin to isolate cDNAs encoding calmodulin-binding proteins. Among several clones isolated, a kinesin-like gene (TCK1) that encodes a calmodulin-binding kinesin-like protein was obtained. The TCK1 cDNA encodes a protein with 1265 amino acid residues. Its structural features are very similar to those of known kinesin heavy chains and kinesin-like proteins from plants and animals, with one distinct exception. Unlike other known kinesin-like proteins, TCK1 contains a calmodulin-binding domain which distinguishes it from all other known kinesin genes. Escherichia coli-expressed TCK1 binds calmodulin in a Ca(2+)-dependent manner. In addition to the presence of a calmodulin-binding domain at the carboxyl terminal, it also has a leucine zipper motif in the stalk region. The amino acid sequence at the carboxyl terminal of TCK1 has striking homology with the mechanochemical motor domain of kinesins. The motor domain has ATPase activity that is stimulated by microtubules. Southern blot analysis revealed that TCK1 is coded by a single gene. Expression studies indicated that TCKI is expressed in all of the tissues tested. Its expression is highest in the stigma and anther, especially during the early stages of anther development. Our results suggest that Ca(2+)/calmodulin may play an important role in the function of this microtubule-associated motor protein and may be involved in the regulation of microtubule-based intracellular transport.

Wang, W.↗

NASA NTRS1998

Droplet Combustion Experiment

Liquid fuel combustion provides a major portion of the world's energy supply. In most practical combustion devices, liquid burns after being separated into a droplet spray. Essential to the design of efficient combustion systems is a knowledge of droplet combustion behavior. The microgravity environment aboard spacecraft provides an opportunity to investigate the complex interactions between the physical and chemical combustion processes involved in droplet combustion without the complications of natural buoyancy. Launched on STS-83 and STS-94 (April 4 and July 1, 1997), the Droplet Combustion Experiment (DCE) investigated the fundamentals of droplet combustion under a range of pressures (0.25 to 1 atm), oxygen mole fractions (<0.5), and droplet sizes (1.5 to 5 mm). Principal DCE flight hardware features were a chamber to supply selected test environments, the use of crew-inserted bottles, and a vent system to remove unwanted gaseous combustion products. The internal apparatus contained the droplet deployment and ignition mechanisms to burn single, freely deployed droplets in microgravity. Diagnostics systems included a 35-mm high-speed motion picture camera (see the following sequence of photos) with a backlight to photograph burning droplets and a camcorder to monitor experiment operations. Additional diagnostics included an ultraviolet-light-sensitive CCD (charge couple discharge) camera to obtain flame radiation from hydroxyl radicals (see the final figure) and a 35-mm SLR (single-lens-reflex) camera to obtain color still photographs of the flames.

Nayagam, Vedha↗

NASA NTRS2015

Air Traffic Management Technology Demonstration (ATD-1) - Concept to Operations

In order to enable arrival management concepts and solutions in a NextGen environment, ground-based sequencing and scheduling functions have been developed to support metering operations in the National Airspace System. These sequencing and scheduling algorithms as well as tools are designed to aid air traffic controllers in developing an overall arrival strategy. The research presented provides an outline of the challenge of bringing time based scheduling to the terminal environment and the steps that were taken to bring the technology from concept to testing in an operational environment, and finally acquisition by the FAA. ATM Technology Demonstration-1 (ATD-1), is a set of technologies that provides an efficient solution for managing arrival aircraft beginning from just prior to top-of-descent and continuing down to the runway. The ATD-1 suite of technologies has been tested separately, and each has demonstrated benefits in throughput, delay, and fuel-efficiency. Together, the integration of terminal arrival tools allow arrival aircraft to safely fly closer together on more fuel-efficient routes to increase capacity, and reduce delay, fuel burn, noise, and greenhouse gas emissions.

DEMO↗

NASA NTRSDate not supplied

Flow Boiling and Condensation Experiment: Flow Boiling in a Rectangular Channel with Subcooled Inlet Conditions in Microgravity

Two-phase thermal management subsystems that take advantage of both the sensible and latent heat of a working fluid can potentially yield significant enhancements in overall performance by adopting heat transfer processes that are based on phase transition like boiling and condensation. Performance of terrestrial two-phase flow systems may be predictable because the hydrodynamic and body forces are understood, however, in microgravity, which is predominant during planetary space travel, forces that are masked by the strong body force on Earth (gravitational or buoyancy force) reappear with different magnitude and influence. The need arose for a facility that provides for two-phase flow with phase transition testing in microgravity. The Flow Boiling and Condensation Experiment (FBCE) is a facility that was launched to the International Space Station in August of 2021 and is in operation since February of 2022. This facility enables investigators to perform two-phase flow and phase transition research in flow boiling and condensation. Along with the test module that is experiment specific, the FBCE system consists of the fluid, avionics, and software subsystems. Currently two test modules, namely, the Flow Boiling Module (FBM) and the Condensation Module for Heat Transfer (CM-HT) are available. A third module, the Transfer Line test Module (TL) is being developed. The fluid subsystem conditions and delivers the fluid at the desired thermodynamic state to the test module. It consists of two fluid modules and a heater module that are connected by flex hoses for fluid circulation and by data and electrical cables for control and data acquisition. Two avionics modules acquire pressure and temperature data from various sensors in the flow loop. For FBM, a high-speed camera is available to acquire images of the boiling process. Experiments are operated autonomously by software and are based on an Experiment Parameters Master Table (EPMT) that is uploaded to ISS and is executed by the FBCE flight software. This presentation briefly introduces the objectives of FBCE and provides a system description of the experiment onboard of the ISS/Fluid Integrated Rack (FIR). Results of the test campaign carried out using the FBM are presented. Specifically, microgravity flow boiling of n-perfluorohexane (test fluid) is discussed with subcooled inlet conditions in a single-side-heated rectangular channel of dimensions 114.6-mm heated length, 2.5-mm heated width, and 5.0-mm height. Key operating parameters investigated are mass velocity (199.90 – 3200.13 kg/m2s), inlet subcooling (0.10 – 45.76°C), and inlet pressure (113.30 – 164.29 kPa). Image sequences acquired via high-speed-video are shown to elucidate the interfacial flow physics. The effects of various parameters on flow boiling heat transfer in microgravity, from the onset of boiling to the critical heat flux are discussed. Heat transfer results are presented in terms of flow boiling curves, streamwise profiles of wall temperature and heat transfer coefficient, and parametric trends of local and averaged heat transfer coefficient, and the critical heat flux.

Two-phase flow and phase transition↗

NASA NTRS2024

Investigating the Physical Modification of the Bennu Sample During Entry, Descent, and Landing

On September 24, 2023, the OSIRIS-REx Sample Return Capsule (SRC) entered Earth’s atmosphere and landed in the Utah Test and Training Range (UTTR). Preliminary examination of the returned Bennu sample has confirmed that OSIRIS-REx sample mass exceeds the mission requirement of 60 g of material. The sample consists of particles that range from a few centimeters to microscopic fines. During the SRC’s entry, descent, and landing (EDL) sequence, it may have experienced (i) peak decelerations of 10s of g (ii) tumbling, and (iii) touchdown at approximately 10 m/s, which could have induced physical modification of the sample. In addition, the act of sampling may have altered or biased the physical properties of the collected materials. Here, we investigate the likelihood and extent of physical modification of the sample between collection and return using observations and modeling. This work addresses the mission’s hypothesis 12, which concerns, in part, the modification of the sample during collection and Earth entry.

asteroid↗

NASA NTRSDate not supplied

Building Automation System Replacement and Improvements during Concurrent Integration and Testing for the Spacecraft Systems Development and Integration Facility

The Spacecraft Systems Development and Integration Facility (SSDIF), the premier cleanroom for NASA Goddard Space Flight Center (GSFC), has gone through multiple iterations of changes to its building automation system (BAS). NASA flight projects require more stringent environmental controls for temperature, humidity, air exchange rates, and pressurization. This led to increasingly difficult circumstances with replacing and improving the BAS during Integration and Testing (I&T) operations. The SSDIF was partially transitioned to a new BAS during November and December of 2023. Transition occurred in a live environment, while regular I&T activities resumed for the Roman Space Telescope. This resulted in the development and execution of a comprehensive method of procedure. Strategies such as sequencing equipment transition, and using temporary controls for critical equipment, avoided detriments to cleanroom environment. While the new BAS was being built, data points on the previous system would become obsolete, leading to data gaps and uncertainties. Critical data points were distinguished and trended on the new system, providing key insights on equipment functionality, while the BAS was completed. Control hunting and environmental deviations were persistent cases with the SSDIF. In some instances, proportional-integral-derivative (PID) tuning was implemented on current logic to bring smooth PID loop hunting. In other situations, controls logic was completely altered by changing setpoints or sequences. This provided greater laminar flow, more stable pressurization, and improved temperature and humidity controls. This paper documents the transitionary procedure to a new BAS, the improvements made, and challenges faced when altering a BAS during I&T operations.

Cleanroom↗

DOE OSTI2022

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2020

Circulating miRNA Signature Predicts Health Risks Associated with Cancer and Spaceflight

Biological risks associated with space radiation and microgravity are major concerns for long-term space travel. Through a Systems Biology approach, our previous NASA work has shown both TGF signaling pathways and miRNAs have a critical impact on defining health risks with and without space irradiation. We hypothesize that circulating microRNA (miRNA) signatures are driving microvascular disease and muscle degeneration associated with accelerating aging and will be enhanced by exposure to the space environment (radiation and microgravity). We are investigating this hypothesis with both in vivo and in vitro models to test novel antagonist therapies to these miRNA signatures as countermeasures to reduce space radiation-induced health risks. A comprehensive Systems Biology approach is utilized to examine the influence by high atomic number by high (H) atomic number (Z) and energy (E) (HZE) irradiation. To simulate low-dose exposure due to galactic cosmic rays (GCR), we used ions, energy, and doses determined by a NASA consensus formula of 7 different ions to represent GCR (referred to as GCR sim model). To similate high-dose radiation exposure due to solar particle events (SPE), we used a solar particle event (SPE) sim model which gave a total dose of 1Gy protons with energy ranges from 50MeV to 150MeV. C57BL/6 wild-type female mice were utilized for the irradiations with our established simulated microgravity model (hindlimb suspension model) and an in vitro 3D microvasculature tissue model under simulated microgravity (clinostat) conditions was also irradiated. To expand on the circulating miRNA signature determined from our preliminary data, we determined a group of conserved miRNAs which are commonly being regulated in the majority of the organs and tissues throughout the host using our established techniques. MiRNA-sequencing was done on serum (at time of sacrifice), liver, heart, and muscle (soleus muscle) tissue for all radiation groups. Additional validation of the key miRNAs was performed by droplet digital PCR (ddPCR). This revealed a key circulating miRNA signature (consisting of multiple miRNAs) impacting cardiovascular and muscular disease risk. Further in vitro experiments with CRISPR/Cas9 system to knockout the key miRNA signatures, novel self-delivering antagomirs, overexpression of the miRNAs test the functional impact of the miRNA signatures on both microvascular disease and muscle degeneration due to space irradiation. The current work has started to allow the possible development of a novel minimally invasive miRNA based radioprotector to be used as a countermeasure for space radiation. Collectively, understanding of how whole body space radiation impacts microvascular and tissue degeneration through circulating miRNAs will greatly enhance health risk prognostication and provide possible new mechanisms for protection against space radiation. This work is supported by the Translational Research Institute through NASA Cooperative Agreement NNX16AO69A (T-0404) awarded to AB.

Beheshti, Afshin↗

DOE OSTI2021

Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning

The analytical description of path-dependent elastic-plastic responses of a granular system is highly complicated because of continuously evolving microstructures and strain localisation within the system undergoing deformation. This study offers an alternative to the current analytical paradigm by developing micromechanics-informed machine-learning based constitutive modelling approaches for granular materials. A set of critical variables associated with the constitutive behaviour of granular materials are identified through an incremental stress-strain relationship analysis. Depending on the strategy to exploit the priori micromechanical knowledge, three different training strategies are explored. The first model uses only the measurable external variables to make stress predictions; the second model utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a series of sub-mappings, and the third model explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced Gated Recurrent Unit (GRU). These three models show satisfactory agreement with unseen test specimens based on multi-directional loading cases. The basic features and potential applications of each model are explained. Lastly, the key factors for constitutive training and limitations of the current work are also discussed in detail.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A combinatorially complete epistatic fitness landscape in an enzyme active site

Protein engineering often targets binding pockets or active sites which are enriched in epistasis—nonadditive interactions between amino acid substitutions—and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences—a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.

biocatalysis↗

DOE OSTI2025

Spatiotemporal predictions of toxic urban plumes using deep learning

Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets. In emergency response situations, alternative methods are needed that can run quickly and adequately capture important spatiotemporal features. Here, we present a novel deep learning model called ST-GasNet inspired by the mathematical equations that govern the behavior of plumes as they disperse through the atmosphere. ST-GasNet learns the spatiotemporal dependencies from a limited set of temporal sequences of ground-level toxic urban plumes generated by a high-resolution large eddy simulation model. On independent sequences, ST-GasNet accurately predicts the late-time spatiotemporal evolution, given the early-time behavior as an input, even when a building splits a large plume into smaller plumes. By incorporating large-scale wind boundary condition information, ST-GasNet achieves a prediction accuracy of at least 90% on test data for the entire prediction period.

Civil and Environmental Engineering↗

DOE OSTI2024

Spot pattern welding scanning strategy for sensor embedding and residual stress reduction in laser-foil-printing additive manufacturing

Here, this paper aims to present spot pattern welding (SPW) as a scanning strategy for laser-foil-printing (LFP) additive manufacturing (AM) in place of the previously used continuous pattern welding (CPW) (line-raster scanning). The SPW strategy involves generating a sequence of overlapping spot welds on the metal foil, allowing the laser to form dense and uniform weld beads. This in turn reduces thermal gradients, promotes material consolidation and helps mitigate process-related risks such as thermal cracking, porosity, keyholing and Marangoni effects. 304L stainless steel (SS) feedstock is used to fabricate test specimens using the LFP system. Imaging techniques are used to examine the melt pool dimensions and layer bonding. In addition, the parts are evaluated for residual stresses, mechanical strength and grain size. Compared to CPW, SPW provides a more reliable heating/cooling relationship that is less dependent on part geometry. The overlapping spot welds distribute heat more evenly, minimizing the risk of elevated temperatures during the AM process. In addition, the resulting dense and uniform weld beads contribute to lower residual stresses in the printed part. To the best of the authors’ knowledge, this is the first study to thoroughly investigate SPW as a scanning strategy using the LFP process. In general, SPW presents a promising strategy for securing embedded sensors into LFP parts while minimizing residual stresses.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A Review of TSUNAMI Applications

The TSUNAMI-1D and TSUNAMI-3D sequences for generating k eff sensitivity coefficients were first released in SCALE 5 in 2004. Several other tools were introduced in the same release, including primarily the TSUNAMI-IP code for uncertainty analysis and similarity assessments. The TSUNAMI sequences added capabilities for identifying important reactions and data for systems using sensitivity coefficients and also introduced new quantitative tools for rigorously assessing the similarity of systems. Over time, the capabilities of these tools have grown, and their use has expanded into other areas such as nuclear data assessment and nuclear covariance data testing. This paper discusses applications of the TSUNAMI tools for sensitivity calculations, sensitivity/uncertainty (S/U)-based validation, and nuclear data testing. This paper is one of a series of papers at this conference on the development and use of TSUNAMI tools. The other papers address the development of the TSUNAMI methods and the use of TSUNAMI for critical experiment design and optimization.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

An improved dataset for predicting mammal infecting viruses from genetic sequence information

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1985

Fatigue Damage in Notched Composite Laminates Under Tension-Tension Cyclic Loads

The results are given of an investigation to determine the damage states which develop in graphite epoxy laminates with center holes due to tension-tension cyclic loads, to determine the influence of stacking sequence on the initiation and interaction of damage modes and the process of damage development, and to establish the relationships between the damage states and the strength, stiffness, and life of the laminates. Two quasi-isotropic laminates were selected to give different distributions of interlaminar stresses around the hole. The laminates were tested under cyclic loads (R=0.1, 10 Hz) at maximum stresses ranging between 60 and 95 percent of the notched tensile strength.

Stinchcomb, W. W.↗