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DOE OSTI2023

Molecular determinants of cross-reactivity and potency by VH3-33 antibodies against the Plasmodium falciparum circumsporozoite protein

IGHV3-33 -encoded antibodies are prevalent in the human humoral response against the Plasmodium falciparum circumsporozoite protein (PfCSP). Among VH3-33 antibodies, cross-reactivity between PfCSP major repeat (NANP), minor (NVDP), and junctional (NPDP) motifs is associated with high affinity and potent parasite inhibition. However, the molecular basis of antibody cross-reactivity and the relationship with efficacy remain unresolved. Here, we perform an extensive structure-function characterization of 12 VH3-33 anti-PfCSP monoclonal antibodies (mAbs) with varying degrees of cross-reactivity induced by immunization of mice expressing a human immunoglobulin gene repertoire. We identify residues in the antibody paratope that mediate cross-reactive binding and delineate four distinct epitope conformations induced by antibody binding, with one consistently associated with high protective efficacy and another that confers comparably potent inhibition of parasite liver invasion. Our data show a link between molecular features of cross-reactive VH3-33 mAb binding to PfCSP and mAb potency, relevant for the development of antibody-based interventions against malaria.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Automated Construction of Artificial Lattice Structures with Designer Electronic States

Manipulating matter with a scanning tunneling microscope (STM) enables the creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM tip, constrains the exploration of diverse configurations and limits the size of the designed features. In this study, we present a reinforcement learning (RL)-based framework for creating artificial structures by spatially manipulating carbon monoxide (CO) molecules on a copper substrate by using the STM tip. The automated workflow combines molecule detection and manipulation, employing deep-learning-based object detection to locate CO molecules and linear assignment algorithms to allocate these molecules to designated target sites. We initially perform molecule maneuvering based on randomized parameter sampling for sample bias, tunneling current set point, and manipulation speed. This data set is then structured into an action trajectory used to train an RL agent. The model is subsequently deployed on the STM for real-time fine-tuning of the manipulation parameters during structure construction. Our approach incorporates path-planning protocols coupled with active drift compensation to enable atomically precise fabrication of structures with significantly reduced human input while realizing larger-scale artificial lattices with the desired electronic properties. Furthermore, using our approach, we demonstrate the automated construction of an extended artificial graphene lattice and confirm the existence of a characteristic Dirac point in its electronic structure. Further challenges regarding the RL-based structural assembly scalability are discussed.

Algorithms↗

DOE OSTI2023

Smart Data Mapping for Connecting Power System Model and Geospatial Data

Knowing the geospatial locations of power system model elements is the foundation for analyzing system vulnerability to natural hazards and connecting loads with end users and their communities. However, power system models and geospatial data for power grid assets may have been developed asynchronously without close coordination. Creating a direct mapping between the two may be a challenging task, considering heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect power system model elements and geospatial data for transmission network, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.

Resilience, geospatial data, grid transmission net↗

DOE OSTI2016

An ensemble model of QSAR tools for regulatory risk assessment

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. In conclusion, this feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Automatic Waveform Quality Control for Surface Waves Using Machine Learning

Surface-wave seismograms are widely used by researchers to study Earth’s interior and earthquakes. To extract information reliably and robustly from a suite of surface waveforms, the signals require quality control screening to reduce artifacts from signal complexity and noise. This process has usually been completed by human experts labeling each waveform visually, which is time consuming and tedious for large data sets. We explore automated approaches to improve the efficiency of waveform quality control processing by investigating logistic regression, support vector machines, K-nearest neighbors, random forests (RF), and artificial neural networks (ANN) algorithms. To speed up signal quality assessment, we trained these five machine learning (ML) methods using nearly 400,000 human-labeled waveforms. The ANN and RF models outperformed other algorithms and achieved a test accuracy of 92%. We evaluated these two best-performing models using seismic events from geographic regions not used for training. The results show that the two trained models agree with labels from human analysts but required only 0.4% of the time. Although the original (human) quality assignments assessed general waveform signal-to-noise, the ANN or RF labels can help facilitate detailed waveform analysis. Our investigations demonstrate the capability of the automated processing using these two ML models to reduce outliers in surface-wave-related measurements without human quality control screening.

58 GEOSCIENCES↗

DOE OSTI2024

Improving Self-Driving Labs: Quantifying System-Level Experiment Repeatability and Broadening Instrument-Level Compatibility

Modular Autonomous Research System (MARS) is a self-driving laboratory (SDL) which performs wet-lab science with peptide-lanthanide combinations in an automated and, ultimately, an autonomous manner to aid in soil analysis for domestic lithium mining. Autonomous experimentation involves automated experimentation, experiment planning, and active learning. MARS consists of a 6-axis robotic arm (UR5e) on a linear rail, pipette robots (Opentrons 2), and microplate readers. These components transport, operate on, and collect data with chemical solutions in standard labware. For effective autonomy, MARS must perform system-level labware operations repeatably, plan experiments autonomously, and be portable between research-domains. Repeatability is evaluated by labware placement precision, such that future operations can properly locate labware, as well as the elapsed time, so that low variance mean estimates of experiment duration can inform high-level researcher decision making. Autonomous experiment planning is the next step to decouple experimentation from human management; however, there is a conflict between the ideal system-level experiment goals and the constraints imposed by instruments’ limitations. Sub-domain portability is a long-term goal to extend MARS’ research beyond the chemistry of peptide-lanthanide binding to other sub-domains without having to invest significant overhead to system retrofitting. To address these goals, we manually trained the robotic arm labware placement and modelled statistical failurerate and uncertainty Additionally, we benchmarked the duration and variance of each experiment sub-operation as a heuristic for research decision making. Next, we use a parameterized geometric program (PGP) approach to design experiments that optimize system-level objectives and satisfy instrument-level constraints. Lastly, we proposed a Python framework to maximize MARS’ extensibility to other scientific sub-domains through a JSON-based experiment specification.

36 MATERIALS SCIENCE↗

NASA NTRS2015

Fall 2015 NASA Internship, and Space Radiation Health Project

This fall, I was fortunate enough to have been able to participate in an internship at NASA's Lyndon B. Johnson Space Center. I was placed into the Human Health & Performance Directorate, where I was specifically tasked to work with Dr. Zarana Patel, researching the impacts of cosmic level radiation on human cells. Using different laboratory techniques, we were able to examine the cells to see if any damage had been done due to radiation exposure, and if so, how much damage was done. Cell culture samples were exposed at different doses, and fixed at different time points so that we could accumulate a large pool of quantifiable data. After examining quantifiable results relative to the impacts of space radiation on the human body at the cellular and chromosomal level, researchers can defer to different areas of the space program that have to do with astronaut safety, and research and development (extravehicular mobility unit construction, vehicle design and construction, etc.). This experience has been very eye-opening, and I was able to learn quite a bit. I learned some new laboratory techniques, and I did my best to try and learn new ways to balance such a hectic work and school schedule. I also learned some very intimate thing about working at NASA; I learned that far more people want to watch you succeed, rather than watch you fail, and I also learned that this is a place that is alive with innovators and explorers - people who have a sole purpose of exploring space for the betterment of humanity, and not for any other reason. It's truly inspiring. All of these experiences during my internship have impacted me in a really profound way, so much that my educational and career goals are completely different than when I started. I started out as a biotechnology major, and I discovered recently toward the end of the internship, that I don't want to work in a lab, nor was I as enthralled by biological life sciences as a believed myself to be. Taking that all into consideration, I've actually changed my major to mechanical engineering. I discovered that I enjoy building things, and I enjoy learning about materials and interactions between different things. And I quickly became obsessed with rocket and aerospace engineering, so I've decided that after a mechanical engineering degree, I will be pursuing an advanced degree in aerospace engineering. One final way that I was effected by this internship, is that I discovered that I don't want to have a career at NASA. I love this agency with all of my heart, but I refuse to allow my innovation to be bound by a scientifically illiterate congress. As such, I have decided to pursue commercial aerospace companies, such as Space, XCOR, Masten Space Systems, Orbital ATK, and many, many, more. Maybe one day I'll end up back here. I believe in what this agency is doing with my whole heart, and it's unfortunate to see them curtailed in some capacities as a result of budgetary constraints, brought on by people who don't fully understand the effort behind putting people in to space. All in all, this experience has been the best experience of my life - literally a childhood dream came true during this experience - and I cannot adequately explain how grateful I am to have been here for the past sixteen weeks.

Patience, Luke↗

NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden C. Szeto↗

NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

Aiden Szeto↗

NASA NTRS2003

Investigating the interaction between the homeostatic and circadian processes of sleep-wake regulation for the prediction of waking neurobehavioural performance

The two-process model of sleep regulation has been applied successfully to describe, predict, and understand sleep-wake regulation in a variety of experimental protocols such as sleep deprivation and forced desynchrony. A non-linear interaction between the homeostatic and circadian processes was reported when the model was applied to describe alertness and performance data obtained during forced desynchrony. This non-linear interaction could also be due to intrinsic non-linearity in the metrics used to measure alertness and performance, however. Distinguishing these possibilities would be of theoretical interest, but could also have important implications for the design and interpretation of experiments placing sleep at different circadian phases or varying the duration of sleep and/or wakefulness. Although to date no resolution to this controversy has been found, here we show that the issue can be addressed with existing data sets. The interaction between the homeostatic and circadian processes of sleep-wake regulation was investigated using neurobehavioural performance data from a laboratory experiment involving total sleep deprivation. The results provided evidence of an actual non-linear interaction between the homeostatic and circadian processes of sleep-wake regulation for the prediction of waking neurobehavioural performance.

Non-NASA Center↗

NASA NTRS2001

In vitro modeling of human tibial strains during exercise in micro-gravity

Prolonged exposure to micro-gravity causes substantial bone loss (Leblanc et al., Journal of Bone Mineral Research 11 (1996) S323) and treadmill exercise under gravity replacement loads (GRLs) has been advocated as a countermeasure. To date, the magnitudes of GRLs employed for locomotion in space have been substantially less than the loads imposed in the earthbound 1G environment, which may account for the poor performance of locomotion as an intervention. The success of future treadmill interventions will likely require GRLs of greater magnitude. It is widely held that mechanical tissue strain is an important intermediary signal in the transduction pathway linking the external loading environment to bone maintenance and functional adaptation; yet, to our knowledge, no data exist linking alterations in external skeletal loading to alterations in bone strain. In this preliminary study, we used unique cadaver simulations of micro-gravity locomotion to determine relationships between localized tibial bone strains and external loading as a means to better predict the efficacy of future exercise interventions proposed for bone maintenance on orbit. Bone strain magnitudes in the distal tibia were found to be linearly related to ground reaction force magnitude (R(2)>0.7). Strain distributions indicated that the primary mode of tibial loading was in bending, with little variation in the neutral axis over the stance phase of gait. The greatest strains, as well as the greatest strain sensitivity to altered external loading, occurred within the anterior crest and posterior aspect of the tibia, the sites furthest removed from the neutral axis of bending. We established a technique for estimating local strain magnitudes from external loads, and equations for predicting strain during simulated micro-gravity walking are presented.

NASA Discipline Musculoskeletal↗

DOE OSTI2023

Encoding integers and rationals on neuromorphic computers using virtual neuron

Abstract Neuromorphic computers emulate the human brain while being extremely power efficient for computing tasks. In fact, they are poised to be critical for energy-efficient computing in the future. Neuromorphic computers are primarily used in spiking neural network–based machine learning applications. However, they are known to be Turing-complete, and in theory can perform all general-purpose computation. One of the biggest bottlenecks in realizing general-purpose computations on neuromorphic computers today is the inability to efficiently encode data on the neuromorphic computers. To fully realize the potential of neuromorphic computers for energy-efficient general-purpose computing, efficient mechanisms must be devised for encoding numbers. Current encoding mechanisms (e.g., binning, rate-based encoding, and time-based encoding) have limited applicability and are not suited for general-purpose computation. In this paper, we present the virtual neuron abstraction as a mechanism for encoding and adding integers and rational numbers by using spiking neural network primitives. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware. We estimate that the virtual neuron could perform an addition operation using just 23 nJ of energy on average with a mixed-signal, memristor-based neuromorphic processor. We also demonstrate the utility of the virtual neuron by using it in some of the μ -recursive functions, which are the building blocks of general-purpose computation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Foreword Special issue on the Kilopower Project, Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Test

This special issue of Nuclear Technology contains full-length, peer-reviewed papers describing the design work leading up to and the results of the Kilowatt Reactor Using Stirling TechnologY (KRUSTY) test. KRUSTY was the centerpiece of the National Aeronautics and Space Administration (NASA) Kilopower Project to design, build, and test a space nuclear reactor. This test was the first such test since the end of the Space Nuclear Auxiliary Power (SNAP) project at the end of the 1960s. In this issue, the introduction paper presents the goals of the Kilopower Project and the potential missions this reactor concept could serve in NASA. Kilopower was intended to serve both human exploration needs on planetary surfaces as well as science needs for deep-space exploration. The design work for the experiment by Poston and the power conversion development by Gibson present the pre-work required to perform the eventual KRUSTY test. A paper on regulatory analysis follows, to show the path used to gain approval of the proposed experiment. Then, the early zero-power critical experiments are presented by Sanchez and Grove. These experiments were essential data used to enhance model predictions prior to the high-temperature test. Next, Poston presents the three experiments (warm criticals) that increase the temperature in an incremental fashion prior to the final experiment. These experiments were used to achieve final regulatory approval of the final high-temperature experiment. The last paper by Poston presents the results of the steady-state and transient testing of the reactor at full power and at the design temperature. These results show that the reactor design and as-built experiment met all of the requirements that NASA had developed for the system. Any experiment of this magnitude was accomplished only because of the hard work and dedication of a large number of people at multiple institutions, including the NASA Glenn Research Center, NASA Marshall Space Flight Center, Y-12 National Security Site, Los Alamos National Laboratory, and the Nevada National Security Site prime contractor (formerly National Security Technologies and now the Mission Support and Test Services). The project was jointly funded by the Space Technology Mission Directorate at NASA and the Criticality Safety Program at the National Nuclear Security Administration (NNSA). A special thanks to Jerry McKamy and Angela Chambers at the NNSA and Lee Mason and Don Palac at NASA for their support and guidance. For the authors and multitude of staff that performed this work, the passion for space nuclear reactors was the key to making this experiment a reality.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2018

GHOST (Grid Hardware Open Source Testbed) [SWR-18-32]

The software was developed for the purpose of Microgrid Controller Procurement where multiple Microgrid Controller vendors were competing to prove the best performance. Each microgrid controller was connected to Real-Time Simulator (Opal-RT) where the Real-Time model of microgrid was simulated. Microgrid controllers were able to control most of the assets in the model. To be able to fairly compare controller's performance each controller was evaluated using exactly the same model, with the same initial conditions and exactly the same test sequence. The microgrid evaluation test sequence lasted 100 minutes and consist of various grid disturbances to which a microgrid controller must respond. The microgrid and power system will experience multiple types of disturbances while performing dispatch functions. Many functions are tested in grid-connected and islanded modes such as unexpended failures of generators, energy storage trip events, and large load fluctuations. The test sequence focuses on fault scenarios that cause the highest short-circuit fault current contribution; force a swing bus or generator capable of being a swing bus offline; cause the most reconfigurations; and cause conflicting objectives for the microgrid controller. During the test sequence execution multiple datapoints are being recorded and stored. During data postprocessing key performance parameters are extracted allowing quantitative comparison of performance on multiple aspects including: resiliency, fuel utilization, grid interconnection, ancillary services and power quality and eventually after summing all key performance parameters by comparing the overall result. To be able to perform such testing multiple tools had to be developed included in this repository including: - Real time model of microgrid (Urban-Banshee, Remote-smaller model) - Test sequence configuration tools - Test execution real-time scripts - Post processing scripts - Human Machine Interface software

Koralewicz, Przemyslaw↗

DOE OSTI2017

Computational Approach for Improving Three-Dimensional Sub-Surface Earth Structure for Regional Earthquake Hazard Simulations in the San Francisco Bay Area

In our Exascale Computing Project (ECP) we seek to simulate earthquake ground motions at much higher frequency than is currently possible. Previous simulations in the SFBA were limited to 0.5-1 Hz or lower (Aagaard et al. 2008, 2010), while we have recently simulated the response to 5 Hz. In order to improve confidence in simulated ground motions, we must accurately represent the three-dimensional (3D) sub-surface material properties that govern seismic wave propagation over a broad region. We are currently focusing on the San Francisco Bay Area (SFBA) with a Cartesian domain of size 120 x 80 x 35 km, but this area will be expanded to cover a larger domain. Currently, the United States Geologic Survey (USGS) has a 3D model of the SFBA for seismic simulations. However, this model suffers from two serious shortcomings relative to our application: 1) it does not fit most of the available low frequency (< 1 Hz) seismic waveforms from moderate (magnitude M 3.5-5.0) earthquakes; and 2) it is represented with much lower resolution than necessary for the high frequency simulations (> 5 Hz) we seek to perform. The current model will serve as a starting model for full waveform tomography based on 3D sensitivity kernels. This report serves as the deliverable for our ECP FY2017 Quarter 4 milestone to FY 2018 “Computational approach to developing model updates”. We summarize the current state of 3D seismic simulations in the SFBA and demonstrate the performance of the USGS 3D model for a few selected paths. We show the available open-source waveform data sets for model updates, based on moderate earthquakes recorded in the region. We present a plan for improving the 3D model utilizing the available data and further development of our SW4 application. We project how the model could be improved and present options for further improvements focused on the shallow geotechnical layers using dense passive recordings of ambient and human-induced noise.

58 GEOSCIENCES↗

DOE OSTI2024

Testing Protocol Development for the Fracture Toughness of Parts Built with Big Area Additive Manufacturing

The mechanical testing of additively manufactured parts has largely relied on the existing standards developed for traditional manufacturing. While this approach leverages the investment made in current standards development, it inaccurately assumes that the mechanical response of additive manufacturing (AM) parts is identical to that of parts manufactured through traditional processes. When considering thermoplastic, material extrusion AM, the differences in response can be attributed to an AM part’s inherent inhomogeneity caused by porosity, interlayer zones, and surface texture. Additionally, the interlayer bonding of parts printed with large-scale AM is difficult to adequately assess, as much testing is performed such that stress is distributed across many layer interfaces; therefore, the lack of AM-specific standards to assess interlayer bonding is a significant research gap. To quantify interlayer bonding via fracture toughness, double cantilever beam (DCB) testing has been used for some AM materials, and DCB has been generally used for a variety of materials including metal, wood, and laminates. Mode I DCB testing was performed on thermoplastic matrix composites printed with Big Area Additive Manufacturing (BAAM). Of particular interest was the notch shape and deflection speed during testing. The results examine the differences when using two notch types and three deflection speeds. The testing method introduced by the following paper differentiates itself from the ones described in the standards used by modernizing the methodology. This was conducted with the introduction of Digital Image Correlation (DIC) to gather displacement and load data simultaneously without human intervention.

Polymer Science↗

DOE OSTI2018

Performance Visualization for TAU Instrumented Scientific Workflows

In exascale scientific computing, it is essential to efficiently monitor, evaluate and improve performance. Visualization and especially visual analytics are useful and inevitable techniques in the exascale computing era to enable such a human-centered experience. In this ongoing work, we present a visual analytics framework for performance evaluation of scientific workflows. Ultimately, we aim to solve two current challenges: the capability to deal with workflows, and the scalability toward exascale scenario. On the way to achieve these goals, in this work, we first incorporate TAU (Tuning and Analysis Utilities) instrumentation tool and improve it to accommodate workflow measurements. Then we establish a web-based visualization framework, whose back end handles data storage, query and aggregation, while front end presents the visualization and takes user interaction. In order to support the scalability, a few level-of-detail mechanisms are developed. Finally, a chemistry workflow use case is adopted to verify our methods

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2003

Effective Team Support: From Modeling to Software Agents

The purpose of this research contract was to perform multidisciplinary research between CMU psychologists, computer scientists and engineers and NASA researchers to design a next generation collaborative system to support a team of human experts and intelligent agents. To achieve robust performance enhancement of such a system, we had proposed to perform task and cognitive modeling to thoroughly understand the impact technology makes on the organization and on key individual personnel. Guided by cognitively-inspired requirements, we would then develop software agents that support the human team in decision making, information filtering, information distribution and integration to enhance team situational awareness. During the period covered by this final report, we made substantial progress in modeling infrastructure and task infrastructure. Work is continuing under a different contract to complete empirical data collection, cognitive modeling, and the building of software agents to support the teams task.

Remington, Roger W.↗