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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Training Artemis Astronauts to Explore the Moon

To prepare for human exploration of the Moon in the mid-2020s, NASA is developing a program to provide geoscience and planetary science classroom training and geologic eld training to astronauts and other mission personnel. The Artemis geoscience training plan builds on a long legacy: NASA has trained astronauts in geology, planetary science, and geological eld work for more than 50 years. From ~1965-1972, the geology focus for astronauts included orbital Earth observations during the early NASA missions, but the endgame was preparation for the Apollo missions to the Lunar surface [see Phinney (2015), NASA-SP-2015-626; Lofgren et al.(2011);Schmitt, et al. (2011); and El Baz (2011), all from GSA SP483]. From the mid 1970s through2017, the primary purpose for geoscience training was to prepare astronauts to observe theEarth and document a wide variety of Earth processes from orbital platforms (Skylab, SpaceShuttle and International Space Station). Astronaut observations of Earth from the InternationalSpace Station remain an important activity today, but the prospect of exploration of the lunar surface through the Artemis Program lends additional purpose to the geoscience content and eld experiences that we provide the astronauts. We recently completed the geology and eld training with Astronaut Class 22 and are now planning both focused eld exercises and simulations for future Artemis crews, as well as the accompanying classroom lessons on lunar and planetary sciences. The training we offer extends beyond the astronaut office – we partner across operational and engineering organizations at NASA and provide eld work exercises and opportunities to personnel in the Flight Operations Directorate (responsible for the overall training and certification), as well as the exploration Extravehicular Activity Oce (developing spacewalk suits and tools). These partnerships contribute to building a common language and sense of purpose, and also help to ensure that NASA’s geoscience training and science operations are fully integrated with the teams and systems responsible for getting the next generation of astronauts to the surface of the Moon.

Artemis↗
NASA NTRSDate not supplied

How To Train Your Dragon (and Vice Versa) – Human Spaceflight Research and The First Commercial Crews

The International Space Station has been an orbiting laboratory for human spaceflight research for over 20 years. NASA’s human research implementation teams have worked to develop, refine, and execute pre-, in-, and post-flight sessions to collect critical research data that will enable future spaceflight missions. The teams have worked closely with the spaceflight vehicle teams, including those for Soyuz and the now retired Space Shuttle vehicles, to successfully conduct this research. As NASA has transitioned to commercial vehicles for transport of crews to the ISS, the implementation teams have expanded their scope to include the SpaceX and upcoming Boeing vehicles. This session will focus on the unique implementation needs for the first available commercial platform, SpaceX. We will identify implementation adjustments made to accommodate the SpaceX Dragon system, including crew training and preflight baseline data collection, inflight operations tied to landing, and postflight planning and crew testing, as well as offer lessons learned from the past three SpaceX long-duration crew missions. Representatives from the Flight Operations Directorate, including the Astronaut Office and the vehicle recovery team, will also provide their perspectives on this new system, and how it affects their work with the human research community. A panel discussion with dedicated time for Q&A will follow.

Pasha Morshedi↗
DOE OSTI2014

MO-E-18C-05: Global Health Catalyst: A Novel Platform for Enhancing Access to Medical Physics Education and Research Excellence (AMPERE)

Purpose: To develop a platform for catalyzing collaborative global Cancer Care Education and Research (CaRE), with a prime focus on enhancing Access to Medical Physics Education and Research Excellence (AMPERE) Methods: An analysis of over 50 global health collaborations between partners in the U.S. and low and middle income countries (LMIC) in Africa was carried out to assess the models of collaborations in Education and Research and relative success. A survey was carried out with questions including: the nature of the collaboration, how it was initiated, impact of culture and other factors, and recommendations for catalyzing/enhancing such collaborations. An online platform called Global Health Catalyst was developed for enhancing AMPERE. Results: The analysis yielded three main models for global health collaborations with survey providing key recommendations on how to enhance such collaborations. Based on this, the platform was developed, and customized to allow Medical Physicists and other Radiation oncology (RadOnc) professionals interested in participating in Global health to readily do so e.g. teach an online course module, participate in training Medical Physicists or other RadOnc health professionals in LMIC, co-mentor students, residents or postdocs, etc. The growing list of features on the platform also include: a feature to enable people to easily find each other, form teams, operate more effectively as partners from different disciplines, institutions, nations and cultural backgrounds, share tools and technologies, obtain seed funding to develop curricula and/or embark upon new areas of investigation, and participate in humanitarian outreach: remote treatment planning assistance, and participation in virtual Chart Rounds, etc. Conclusion: The developed Global Health Catalyst platform could enable any Medical Physicist or RadoOnc professional interested in global health to readily participate in the Education/training of next generation RadOnc professionals and global health leaders, and enhance AMPERE, especially for LMIC.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2003

A Multi-Use Airborne Research Facility

Much of our progress in understanding the Earth system comes from measurements made in the atmosphere. Aircraft are widely used to collect in situ measurements of the troposphere and lower stratosphere, and they also serve as platforms for many remote sensing instruments. Airborne field measurement campaigns require a capable aircraft, a specially trained support team, a suite of basic instrumentation, space and power for new instruments, and data analysis and processing capabilities (e.g. Veal et al., 1977). However, these capabilities are expensive and there is a need to reduce costs while maintaining the capability to perform this type of research. To this end, NASA entered a Cooperative Agreement with the University of North Dakota (UND) to help support the operations of the UND Cessna Citation research aircraft. This Cooperative Agreement followed in form and substance a previous agreement. The Cooperative Agreement has benefited both NASA and UND. In part because of budget reductions, the NASA Airborne Science Office has elected to take advantage of outside operators of science research platforms to off-load some science requirements (Huning, 1996). UND has worked with NASA to identify those requirements that could be met more cost effectively with the UND platform. This has resulted in significant cost savings to NASA while broadening the base of researchers in the NASA science programs. At the same time, the Agreement has provided much needed support to UND to help sustain the Citation research facility. In this report, we describe the work conducted under this Cooperative Agreement.

Poellot, Michael R.↗
DOE OSTI2017

Scaling Deep Learning on GPU and Knights Landing clusters

The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From an algorithm aspect, current distributed machine learning systems are mainly designed for cloud systems. These methods are asynchronous because of the slow network and high fault-tolerance requirement on cloud systems. We focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original EASGD used round-robin method for communication and updating. The communication is ordered by the machine rank ID, which is inefficient on HPC clusters. First, we redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster \textcolor{black}{than} their existing counterparts (Async SGD, Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design Sync EASGD, which ties for the best performance among all the methods while being deterministic. In addition to the algorithmic improvements, we use some system-algorithm codesign techniques to scale up the algorithms. By reducing the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x speedup over original EASGD on the same platform. We get 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Oakland University Cybersecurity Center (Final Scientific/Technical Report)

This report summarizes the outcomes of Award DE-CR0000023, “Oakland University Cybersecurity Center,” a 31-month project funded by the U.S. Department of Energy Office of Cybersecurity, Energy Security, and Emergency Response (CESER). The project addressed cybersecurity risks facing small and medium-sized manufacturers (SMMs) transitioning to Industry 4.0. The project integrated customer discovery, applied research, and cybersecurity training development. A total of 51 cybersecurity assessments identified significant gaps in baseline practices, incident response, and workforce capability. Research efforts produced a scalable mitigation framework tailored to SMM environments, and workforce analysis identified persistent talent gaps. Eight cybersecurity training modules were developed and deployed via Oakland University’s Professional and Continuing Education (PACE) platform. All objectives were completed, with 98.93% federal budget utilization and cost share exceeding requirements. The project establishes a scalable model for strengthening cybersecurity resilience and workforce capacity across U.S. manufacturing supply chains.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Scale-up Unlearnable Examples Learning with High-performance Computing

Recent advancements in AI models, like ChatGPT, are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources (e.g., a single workstation). To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE’s unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. The use of Summit’s high-performance GPUs, along with the efficiency of the DDP framework, facilitated rapid updates of model parameters and consistent training across nodes. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications. The source code is publicly available at https: // github. com/ hrlblab/ UE_ HPC .

Zhu, Yanfan [Vanderbilt University, Nashville, TN,↗
NASA NTRS2000

Photon-Counting Multikilohertz Microlaser Altimeters for Airborne and Spaceborne Topographic Measurements

We consider the optimum design of photon-counting microlaser altimeters operating from airborne and spaceborne platforms under both day and night conditions. Extremely compact Q-switched microlaser transmitters produce trains of low energy pulses at multi-kHz rates and can easily generate subnanosecond pulse-widths for precise ranging. To guide the design, we have modeled the solar noise background and developed simple algorithms, based on Post-Detection Poisson Filtering (PDPF), to optimally extract the weak altimeter signal from a high noise background during daytime operations. Practical technology issues, such as detector and/or receiver dead times, have also been considered in the analysis. We describe an airborne prototype, being developed under NASA's instrument Incubator Program, which is designed to operate at a 10 kHz rate from aircraft cruise altitudes up to 12 km with laser pulse energies on the order of a few microjoules. We also analyze a compact and power efficient system designed to operate from Mars orbit at an altitude of 300 km and sample the Martian surface at rates up to 4.3 kHz using a 1 watt laser transmitter and an 18 cm telescope. This yields a Power-Aperture Product of 0.24 W-square meter, corresponding to a value almost 4 times smaller than the Mars Orbiting Laser Altimeter (0. 88W-square meter), yet the sampling rate is roughly 400 times greater (4 kHz vs 10 Hz) Relative to conventional high power laser altimeters, advantages of photon-counting laser altimeters include: (1) a more efficient use of available laser photons providing up to two orders of magnitude greater surface sampling rates for a given laser power-telescope aperture product; (2) a simultaneous two order of magnitude reduction in the volume, cost and weight of the telescope system; (3) the unique ability to spatially resolve the source of the surface return in a photon counting mode through the use of pixellated or imaging detectors; and (4) improved vertical and transverse spatial resolution resulting from both (1) and (3). Furthermore, because of significantly lower laser pulse energies, the microaltimeter is inherently more eyesafe to observers on the ground and less prone to internal optical damage, which can terminate a space mission prematurely.

Degnan, John J.↗
DOE OSTI2023

DeepDataProfiler: A Platform and Methodology for the Analysis and Interpretation of Neural Networks

The DeepDataProfiler is a methodology and framework for providing interpretability to trained neural networks. Its approach is to decompose a network into a weighted graph of neurons and synapses and link the components of the graph to human identifiable concepts. By identifying concepts important to the network and tracking the decision process employed by the network, the network becomes more transparent and less like a black box. Spurious decisions and poor generalization strategies can be identified and a measure of trustworthiness can be established.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Smart Building Technology Training Modules for Academic and Professional Education

Smart building technologies are a new suite of resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both college curricula and building professionals’ continuing education, there is a lack of systematic instruction on smart building technologies–topics that include smart building concepts, key components, smart building controls, “Internet of Things” (IoT) devices, and how to integrate multiple energy systems including distributed energy resources (DER). This major gap in smart building education prevents stakeholders from understanding and adopting smart building technologies in building design and operations. Slipstream leads a DOE-funded project developing a semester-long smart building curriculum for college students and adapting the contents into 16 training videos for building professionals and the general public. The education and training cover the drivers and benefits of smart building technologies, key building energy systems, the latest sensor technologies and IoT devices, and focus on topics related to smart building controls (i.e., energy management information systems, smart building control platforms, cybersecurity, grid-interactive-efficient buildings (GEBs), smart building control methods, and occupant-centric control. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content. We also suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies in the real world.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Balanced k -means clustering on an adiabatic quantum computer

Adiabatic quantum computers are a promising platform for efficiently solving challenging optimization problems. Therefore, many are interested in using these computers to train computationally expensive machine learning models. We present a quantum approach to solving the balanced k-means clustering training problem on the D-Wave 2000Q adiabatic quantum computer. In order to do this, we formulate the training problem as a quadratic unconstrained binary optimization (QUBO) problem. Unlike existing classical algorithms, our QUBO formulation targets the global solution to the balanced k-means model. We test our approach on a number of small problems and observe that despite the theoretical benefits of the QUBO formulation, the clustering solution obtained by a modern quantum computer is usually inferior to the solution obtained by the best classical clustering algorithms. Nevertheless, the solutions provided by the quantum computer do exhibit some promising characteristics. We also perform a scalability study to estimate the run time of our approach on large problems using future quantum hardware. Finally, as a final proof of concept, we used the quantum approach to cluster random subsets of the Iris benchmark data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings

In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD. We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization. We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

SU-F-R-22: Malignancy Classification for Small Pulmonary Nodules with Radiomics and Logistic Regression

Purpose: We conducted a retrospective study of Radiomics research for classifying malignancy of small pulmonary nodules. A machine learning algorithm of logistic regression and open research platform of Radiomics, IBEX (Imaging Biomarker Explorer), were used to evaluate the classification accuracy. Methods: The training set included 100 CT image series from cancer patients with small pulmonary nodules where the average diameter is 1.10 cm. These patients registered at Chang Gung Memorial Hospital and received a CT-guided operation of lung cancer lobectomy. The specimens were classified by experienced pathologists with a B (benign) or M (malignant). CT images with slice thickness of 0.625 mm were acquired from a GE BrightSpeed 16 scanner. The study was formally approved by our institutional internal review board. Nodules were delineated and 374 feature parameters were extracted from IBEX. We first used the t-test and p-value criteria to study which feature can differentiate between group B and M. Then we implemented a logistic regression algorithm to perform nodule malignancy classification. 10-fold cross-validation and the receiver operating characteristic curve (ROC) were used to evaluate the classification accuracy. Finally hierarchical clustering analysis, Spearman rank correlation coefficient, and clustering heat map were used to further study correlation characteristics among different features. Results: 238 features were found differentiable between group B and M based on whether their statistical p-values were less than 0.05. A forward search algorithm was used to select an optimal combination of features for the best classification and 9 features were identified. Our study found the best accuracy of classifying malignancy was 0.79±0.01 with the 10-fold cross-validation. The area under the ROC curve was 0.81±0.02. Conclusion: Benign nodules may be treated as a malignant tumor in low-dose CT and patients may undergo unnecessary surgeries or treatments. Our study may help radiologists to differentiate nodule malignancy for low-dose CT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Enhancing laboratory capacity during Ebola virus disease (EVD) heightened surveillance in Liberia: lessons learned and recommendations

Following a declaration by the World Health Organization that Liberia had successfully interrupted Ebola virus transmission on May 9th, 2015; the country entered a period of enhanced surveillance. The number of cases had significantly reduced prior to the declaration, leading to closure of eight out of eleven Ebola testing laboratories. Enhanced surveillance led to an abrupt increase in demand for laboratory services. We report interventions, achievements, lessons learned and recommendations drawn from enhancing laboratory capacity. Using archived data, we reported before and after interventions that aimed at increasing laboratory capacity. Laboratory capacity was defined by number of laboratories with Ebola Virus Disease (EVD) testing capacity, number of competent staff, number of specimens tested, specimen backlog, daily and surge testing capacity, and turnaround time. Using Stata 14 (Stata Corporation, College Station, TX, USA), medians and trends were reported for all continuous variables. Our results show that between May and December 2015, interventions including recruitment and training of eight staff, establishment of one EVD laboratory facility, implementation of ten Ebola GeneXpert diagnostic platforms, and establishment of working shifts yielded an 8-fold increase in number of specimens tested, a reduction in specimens backlog to zero, and restoration of turn-around time to 24 hours. This enabled a more efficient surveillance system that facilitated timely detection and containment of two EVD clusters observed thereafter. In conclusion, effective enhancement of laboratory services during high demand periods requires a combination of context-specific interventions. Building and ensuring sustainability of local capacity is an integral part of effective surveillance and disease outbreak response efforts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Dual-Biometric Human Identification Using Radar Deep Transfer Learning

Accurate human identification using radar has a variety of potential applications, such as surveillance, access control and security checkpoints. Nevertheless, radar-based human identification has been limited to a few motion-based biometrics that are solely reliant on micro-Doppler signatures. This paper proposes for the first time the use of combined radar-based heart sound and gait signals as biometrics for human identification. The proposed methodology starts by converting the extracted biometric signatures collected from 18 subjects to images, and then an image augmentation technique is applied and the deep transfer learning is used to classify each subject. A validation accuracy of 58.7% and 96% is reported for the heart sound and gait biometrics, respectively. Next, the identification results of the two biometrics are combined using the joint probability mass function (PMF) method to report a 98% identification accuracy. To the best of our knowledge, this is the highest reported in the literature to date. Lastly, the trained networks are tested in an actual scenario while being used in an office access control platform to identify different human subjects. We report an accuracy of 76.25%.

47 OTHER INSTRUMENTATION↗
NASA NTRS2006

Initial Results from CALIPSO

CALIPSO will carry the first polarization lidar in orbit, along with infrared and visible passive imagers, and will fly in formation as part of the Afternoon Constellation (A-train). The acquisition of observations which are simultaneous and coincident with observations from other instruments of the A-train will allow numerous synergies to be realized from combining CALIPSO observations with observations from other platforms. In particular, cloud observations from the CALIPSO lidar and the CloudSat radar will complement each other, together encompassing the variety of clouds found in the atmosphere, from thin cirrus to deep convective clouds. CALIPSO has been developed within the framework of a collaboration between NASA and CNES and is currently scheduled to launch, along with the CloudSat satellite, in spring 2006. This paper will present an overview of the CALIPSO mission, including initial results.

Winker, David M.↗
NASA NTRS2015

Circadian Effects on Simple Components of Complex Task Performance

The goal of this study was to advance understanding and prediction of the impact of circadian rhythm on aspects of complex task performance during unexpected automation failures, and subsequent fault management. Participants trained on two tasks: a process control simulation, featuring automated support; and a multi-tasking platform. Participants then completed one task in a very early morning (circadian night) session, and the other during a late afternoon (circadian day) session. Small effects of time of day were seen on simple components of task performance, but impacts on more demanding components, such as those that occur following an automation failure, were muted relative to previous studies where circadian rhythm was compounded with sleep deprivation and fatigue. Circadian low participants engaged in compensatory strategies, rather than passively monitoring the automation. The findings and implications are discussed in the context of a model that includes the effects of sleep and fatigue factors.

circadian effects↗