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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Cross Recurrence Analysis as a Measure of Pilots' Coordination Strategy

When solving problems, multi-person airline crews can choose whether to work together, or to address different aspects of a situation with a divide and conquer strategy. Knowing which of these strategies is most effective may help airlines develop better procedures and training. This paper concentrates on joint attention as a measure of crew coordination. We report results obtained by applying cross recurrence analysis to eye movement data from two-person crews, collected in a flight simulator experiment. The analysis shows that crews exhibit coordinated gaze roughly 16th of the time, with a tendency for the captain to lead the first officers visual attention. The degree to which crews coordinate their gaze is not significantly correlated with performance ratings assigned by instructors; further research questions and approaches are discussed.

non-linear models↗
NASA NTRS2020

Evaluation Issues for a Flight Deck Interface; CAST SE-210 Output 2: Report 4 of 6

This report is part of a series of reports that addresses flight deck design and evaluation, written as a response to loss of control accidents. In particular, this activity is directed at failures in airplane state awareness, in which the pilot loses awareness of the airplane’s energy state or attitude and enters an upset condition. Another report in this series of reports speaks directly to flight deck evaluation methods and metrics for the types of attention and awareness issues that were revealed from the airplane state awareness events. In this report, we describe a wide range of flight deck evaluation issues tied to flight crew performance. The objectives are to establish a framework for thinking about how the flight deck interface should support the performance of the flight crew, and to aid the Federal Aviation Administration (FAA) in identifying relevant human performance issues during the evaluation/certification process. Issues are broken out into sections that cover physical ergonomics, design for usability, data integration and display content, attention and task management, flight crew problem solving, and flight crew teaming. For each issue, we recommend specific ways that the flight deck interface should support the flight crew. For each, we also identify existing 14 CFR Part 25 rules and guidance that are relevant to the issue. This allows the FAA to determine what current regulatory materials can support them in raising the issue with the applicant.

commercial aviation↗
NASA NTRSDate not supplied

TRUST, Trustworthiness and EOSDIS

In recent years there has been considerable attention by the international scientific research and applications community to ensure high quality of data and information management. The terms FAIR (Findable, Accessible, Interoperable, Reusable) data, TRUST (Transparency, Responsibility, User Community, Sustainability, and Technology) principles, and CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) principles have come into vogue during the last decade. NASA has been managing data and information for over 60 years. NASA’s Earth Observing System Data and Information System (EOSDIS) has been in operation for over 25 years, managing most of NASA’s Earth science data. Trustworthiness is a goal that NASA has always strived to achieve or exceed, because it: enables the success of any NASA science mission; inspires general science research and applications; justifies the cost of operations; contributes to the value of NASA’s Open Data Policy; and influences the long term, historical view for the data collection. Given the recent growth of interest in TRUST principles, it is useful to assess and show how NASA’s attention to trustworthiness maps into those principles. This presentation addresses shows how the various steps that have been taken by the Earth Science Data and Information System (ESDIS) Project in the implementation and evolution of EOSDIS map into the TRUST principles.

Remote Sensing↗
NASA NTRSDate not supplied

Flight Trajectory Prediction Based on Hybrid-Recurrent Networks

The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostlyrely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 100-day and 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-LSTM design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicatethe effectiveness of LSTM and GRUcells for state-of-the-art data processing (interpolation). Additionally, GRUs may be quickly trained with limited data, allowing for exacting improvements with optimizer selection. Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value, requiring support from tailored measurements for Air Traffic Management.

Nathan Schimpf↗
NASA NTRS2022

Circular Polarization in Atmospheric Aerosols

Recent technological advances have demonstrated the feasibility of deploying spaceborne optical detectors with full polarimetric capabilities. The measurement of all four Stokes coefficients opens significant new opportunities for atmospheric aerosol studies and applications. While considerable amounts of attention have been dedicated to sensors with sensitivity to the total intensity and linear polarization (represented by Stokes coefficients I, U, Q), there has been less attention to the additional information brought by measuring circular polarization (coefficient V). This report fills this gap in knowledge by providing an overview of aerosol sources of circular polarization in the atmosphere and discusses possible remote sensing signatures. In this manuscript, circularly polarized radiation that results from the interaction of incident unpolarized radiation are considered in three physical settings: optical activity originating in biogenic aerosols, alignment of non-spherical particles in the presence of electrical fields (such as dust, smoke, and volcanic ash) and aerosol multiple scattering effects. Observational and theoretical evidence of, and the settings and conditions for non-zero aerosol circular polarization generated from incident unpolarized radiation are here gathered and discussed. In addition, novel radiative transfer simulations are shown to illustrate notable spectral and other features where circular polarization may provide additional information that is possibly independent from total intensity and linear polarization only observations. Current techniques for detection of aerosol composition (also referred as aerosol type) from space provide limited information. Remote identification of aerosols such as smoke, volcanic ash, and dust particles can only be accomplished with some degree of confidence for moderate to high concentrations. When the same aerosols are found at lower concentrations (but still high enough to be of importance for air quality and cloud formation), these methods often produce ambiguous results. The circular polarization of aerosols is rarely utilized, and we explore its value for improved determination aerosol composition. This study is presented as an overview with a goal to provide a new perspective on an overlooked optical property and to trigger interest in further exploration of this subject.

Aerosol type↗
NASA NTRS2022

Rise and Shine: The Use of Polychromatic Short-Wavelength-Enriched Light to Mitigate Sleep Inertia at Night Following Awaking From Slow-Wave Sleep

Sleep inertia is the brief period of performance impairment and reduced alertness experienced after waking, especially from slow-wave sleep. We assessed the efficacy of polychromatic short-wavelength-enriched light to improve vigilant attention, alertness and mood immediately after waking from slow-wave sleep at night. Twelve participants (six female, 23.3 ± 4.2 years) maintained an actigraphy-confirmed sleep schedule of 8.5 hr for 5 nights, and 5 hr for 1 night prior to an overnight laboratory visit. In the laboratory, participants were awakened from slow-wave sleep, and immediately exposed to either dim, red ambient light (control) or polychromatic short-wavelength-enriched light (light) for 1 hr in a randomized crossover design. They completed a 5-min Psychomotor Vigilance Task, the Karolinska Sleepiness Scale, and Visual Analogue Scales of mood at 2, 17, 32 and 47 min after waking. Following this testing period, lights were turned off and participants returned to sleep. They were awakened from their subsequent slow-wave sleep period and received the opposite condition. Compared with the control condition, participants exposed to light had fewer Psychomotor Vigilance Task lapses (χ 2 [1] = 5.285, p = 0.022), reported feeling more alert (Karolinska Sleepiness Scale: F 1,77 = 4.955, p = 0.029; Visual Analogue Scale alert : F 1,77 = 8.226, p = 0.005), and reported improved mood (Visual Analogue Scale cheerful : F 1,77 = 8.615, p = 0.004). There was no significant difference in sleep-onset latency between conditions following the testing period (t 10 = 1.024, p = 0.330). Our results suggest that exposure to polychromatic short-wavelength-enriched light immediately after waking from slow-wave sleep at night may help improve vigilant attention, subjective alertness, and mood. Future studies should explore the potential mechanisms of this countermeasure and its efficacy in real-world environments.

alertness↗
NASA NTRSDate not supplied

Limitations on the Use of Eye-Tracking Data to Understand Operator Awareness

In the last 20 years, a number of accidents and incidents in commercial aviation have pointed to poor flight crew awareness of basic flight path parameters (e.g., airspeed, bank, pitch). As a result, there is a desire to improve pilot situation awareness and how attention is allocated. Eye-tracking has been a commonly used measure of awareness; it can aid in understanding whether specific indications were fixated, and perhaps how a pilot gathered information (that is, which indications in which sequence). In this paper, we discuss limitations on what eye-tracking data can reveal about pilot awareness and understanding. First, previous studies (e.g., Sarter et al., 2007) have shown that fixation on an indication may not ensure awareness or understanding. Further, an operator may have awareness of information not fixated. Additional measures—such as self-report or control inputs—can help to better establish the extent of pilot awareness and understanding. Second, sequences of fixations (scan patterns) have also become a performance measure. While a small number of recognized scan patterns have been validated for a small set of parameters on the Primary Flight Display (PFD), scan patterns have not been identified to support the broader context of flight path management or flight operations. More important is to understand the full set of drivers underlying the larger pattern of eye fixations; this approach moves away from the idea of well-established scan patterns as a marker of skilled performance and gives a larger role to pilot cognition. Pilots have various reasons to direct attention to specific elements on the interface, such as: - feedback tied to control inputs - a check on compliance with flight path targets - a reaction to an alert or a call out - attempt to understand an unexpected indication - assess progress toward a flight path target The importance of cognition is further implicated in the finding that pilot interviews show that fixating typically is accompanied by expectation; generally, pilots have a strong expectation of what value or indication they will see, which allows more efficient integration of information and an ability to identify indications that suggest an alternative account of the current system state. We will describe a range of eye-tracking measures and how they should and should not be used.

aviation↗
NASA NTRSDate not supplied

Capturing, Analyzing, Maintaining, and Disseminating Shape Memory Material Data Between Information Management Systems

With an increased demand on reducing the time, cost, and effort to develop new materials, Integrated Computational Materials Engineering (ICME) has received widespread attention in various engineering disciplines as a catalyst for significantly reducing experimental testing during the material design process. An ICME approach to design can enable ‘fit-for-purpose’ materials to be realized in engineering applications by incorporating well-understood process-property-performance relationships between the various length and time scales in a material’s structure, enabling material optimization. However, such an approach requires validated multiscale models at the various length scales for a material, which in turn requires a large amount of data, a robust means of storing the data, and the ability to link data to developed material models. The NASA Vision 2040 [1] has identified nine key elements to enabling ICME approaches in system level design, with one being “Data, Information, and Visualization”, thus outlining the importance of a robust information management system for ICME. As the relationship between microstructure, properties, and material performance become better understood and incorporated into multiscale models that can be leveraged in application design, the emergence of new materials with application-driven properties can be realized. One such new material class that has seen growing attention are shape memory materials (SMM), in which a material can transition between a deformed and undeformed state via a reversible phase transformation when subject to a thermal, mechanical, or magnetic load [2]. SMMs have been used widely in aerospace and biomedical industries, including applications such as actuators, low-shock mechanisms, medical staples, braces, and stents [3, 4]. These materials exhibit unique behavior due to their ability to transition between phases, and thus the mechanisms that enable this transition must be captured in a data information management system and incorporated into SMM material models. At NASA Glenn Research Center, the Shape Memory Materials Database (SMMD) Tool has been developed to capture the necessary information that governs SMM material behavior and provide users the ability to select and visualize various SMMs for a specific application [5]. The database contains point-wise data for published SMM materials, along with the pedigree metadata for traceability necessary for a robust information management system. The database is also capable of storing in-house test data performed at NASA GRC by interacting with the developed Shape Memory Alloy (SMA) Analytics tool to extract the necessary point-wise values and populate the database. Although the SMMD Tool offers its users a single, authoritative source for SMM material data that is critical for model development and material design, the full material pedigree of the in-house test data for SMMs is not currently captured and is out of the scope for the SMMD tool. In this work, the schema for capturing SMM test data within the larger NASA GRC ICME Schema [6, 7, 8, 9] will be developed and implemented for thermomechanical tests conducted at NASA GRC. The developed schema will not only store the relevant data needed for the SMMD tool, but also the material pedigree (i.e., production of the bulk material, bulk material analysis, sample cut-out diagrams, sample fabrication procedure, etc.), test pedigree (i.e., test equipment used, measurement systems used, raw test data), and analysis pedigree (i.e., how the data in the SMMD tool is calculated). Furthermore, a Python-based framework will be developed to seamlessly interact between the SMA Analytics and SMMD tools, which will write the full dataset and associated metadata to the GRC Information Management System before passing the required point-wise data to the SMMD tool. Data informatics is a key element of the NASA Vision 2040, which requires not only that data is stored and maintained throughout the material lifecycle, but that the data is also accessible and reusable such that material development efforts can be minimized. Therefore, for an ICME design approach to be realized, a centralized information management system that drives the ICME process must be able to communicate with other databases. The work that will be presented in this presentation will therefore not only demonstrate the ability of NASA GRC’s information management system to capture SMM data, but also its ability to interact with pre-existing tools specialized for such materials.

Data management↗
NASA NTRSDate not supplied

Comparison of Multivariate Time Series Prediction Techniques for Emulating Noah-LSM Soil Moisture Outputs

Land surface models are crucial tools for many earth science applications including numerical weather prediction, water resource and crop monitoring, and climatological analysis. Given a set of atmospheric forcings, seasonal data, and static parameters, models like Noah-LSM solve for land surface quantities including skin temperature, sensible heat flux, and soil moisture. While these calculations are theoretically robust, they are often computationally expensive. Since artificial neural networks (ANNs) are universal function approximators, they can learn to emulate the output of a deterministic numerical model given a time series of input forcings, with the learned ANN having substantially shorter execution time. The ANN could efficiently parameterize other models, generate ensembles, and provide first-guess inputs for retrievals. As such, with the goal of developing a model that efficiently mimics the output of Noah-LSM given NLDAS2 forcings on a region covering much of the central US, we examine and compare several neural network architectures for the multi-horizon multivariate time series forecasting problem. Recent literature includes a diverse set of approaches including autoregressive architectures like LSTM and GRU, parametric and non-parametric statistical predictors (ForecastNet and MQRNN), self-attention (LSTM-attention-LSTM), and temporal convovlution (DeepTCN). We implement several of these models for the Noah-LSM prediction task, highlighting the features and challenges for each and providing practical insight on the training process.

Mitchell Dodson↗
NASA NTRSDate not supplied

Changes in Oculomotor Behavior and PVT Reaction Time During One Night of Sleep Deprivation

INTRODUCTION: The Psychomotor Vigilance Test (PVT) is a widely used objective measure of sustained attention, alertness, and fatigue. Previous studies consistently show that prolonged wakefulness and disrupted sleep patterns lead to increased lapses of attention and slower reaction times. These findings have critical implications for various professions, including healthcare, aviation, transportation safety, and spaceflight. In this study, we employed linear mixed models (LMM) to investigate hourly-measured 5-minute PVT reaction times during one night of total sleep deprivation. Our goal was to characterize the contribution of homeostatic sleep pressure (time awake) and circadian phase (salivary melatonin levels) on the dynamics of PVT reaction time. MATERIALS & METHODS: Data from twelve human participants were used in the analysis. Participants were healthy non-smokers, aged 18 and 40, with normal sleep habits defined as Pittsburg Sleep Quality Index scores < 5, and Morningness – Eveningness Questionnaire scores > 42 and < 58, respectively. Participants followed a constant-routine protocol and arrived at the NASA sleep laboratory approximately 1 to 2 hours after their habitual waking time. Tympanic temperature was measured every 30 min to provide a real-time estimate of circadian phase. Participants were required to stay awake throughout the laboratory experiment, which continued until their tympanic temperature had returned to baseline levels. This approach ensured that we could capture each participant's circadian trough and recovery, typically occurring between 24 and 26 hours after waking. The PVT and saliva melatonin were measured every hour, starting approximately from 3 hours after awakening. More information about the measurement protocol can be found in the publication by Stone et al. We used PVT reaction times between 100 and 10000 ms to calculate Reciprocal Reaction Times (RRTs). LMM models were employed to assess the contribution of time awake and saliva melatonin levels on changes in RRTs. Two models were created: the first model treated time awake as a fixed effect and the second model considered both the salivary melatonin level and time awake as fixed effects. Participants were included as a random effect in both models. We compared the models using an Analysis of Variance (ANOVA). The models were computed using the 'lmer' function from the 'lme4' package in R, and LMM p-values were determined using Satterthwaite's method. RESULTS: We found that time awake had a statistically significant effect on PVT RRTs during one night of sleep deprivation [χ2 = 2132.4, df = 1, p < 0.001]. In addition, the model including both saliva melatonin levels and time awake was statistically significantly better than one that uses just time awake [χ2 = 17.4, df = 1, p < 0.001]. DISCUSSION & CONCLUSION: As expected, these preliminary results suggest that the time awake and circadian phase each contribute to performance reductions as measured by the PVT. Future analyses will explore the contributions of other factors to the model.

sleep deprivation↗
NASA NTRS2024

Cognitive Performance in ISS Astronauts on 6-Month Low Earth Orbit Missions

Introduction: Current and future astronauts will endure prolonged exposure to spaceflight hazards and environmental stressors that could compromise cognitive functioning, yet cognitive performance in current missions to the International Space Station remains critically under-characterized. We systematically assessed cognitive performance across 10 cognitive domains in astronauts on 6-month missions to the ISS. Methods: Twenty-five professional astronauts were administered the Cognition Battery as part of National Aeronautics and Space Administration (NASA) Human Research Program Standard Measures Cross-Cutting Project. Cognitive performance data were collected at five mission phases: pre-flight, early flight, late flight, early post-flight, and late post-flight. We calculated speed and accuracy scores, corrected for practice effects, and derived z-scores to represent deviations in cognitive performance across mission phases from the sample’s mean baseline (i.e., pre-flight) performance. Linear mixed models with random subject intercepts and pairwise comparisons examined the relationships between mission phase and cognitive performance. Results: Cognitive performance was generally stable over time with some differences observed across mission phases for specific subtests. There was slowed performance observed in early flight on tasks of processing speed, visual working memory, and sustained attention. We observed a decrease in risk-taking propensity during late flight and post-flight mission phases. Beyond examining group differences, we inspected scores that represented a significant shift from the sample’s mean baseline score, revealing that 11.8% of all flight and post-flight scores were at or below 1.5 standard deviations below the sample’s baseline mean. Finally, exploratory analyses yielded no clear pattern of associations between cognitive performance and either sleep or ratings of alertness. Conclusions: There was no evidence for a systematic decline in cognitive performance for astronauts on a 6-month missions to the ISS. Some differences were observed for specific subtests at specific mission phases, suggesting that processing speed, visual working memory, sustained attention, and risk-taking propensity may be the cognitive domains most susceptible to change in Low Earth Orbit for high performing, professional astronauts. We provide descriptive statistics of pre-flight cognitive performance from 25 astronauts, the largest published preliminary normative database of its kind to date, to help identify significant performance decrements in future samples.

Data curation↗
NASA NTRSDate not supplied

A Queuing Theory Approach to Pilot-Controller Coordination for m:N Operations

In recent years, attention and interest by industry and researchers has grown in a control paradigm for remotely piloted aircraft termed “m:N operations.” In an m:N operation, a team of m remote pilots in command (RIPCs) collaboratively manage the flights of N aircraft. A consequence of an m:N concept of operations is that the RPICs will have to switch attention from one aircraft to another and from one task to another. Previous research in m:N operations has focused on the workload experienced by an RPIC and their level of situation awareness on their flights. Researchers have found that RPIC workload and situation awareness are generally sensitive to increasing N, although NASA’s Multi-Vehicle (m:N) Working Group has suggested that the driver of workload/situation awareness is the number of exceptions requiring human intervention as opposed to the value of N itself. In any case, a natural antecedent of workload is task load. In this paper, queueing theory is applied to a 1:N Urban Air Mobility (UAM) air taxi operation in order to estimate pilot task load for managing radio communications with air traffic controllers (ATCs) under increasing N. An M/M/1 queueing system is used to model the RIPC’s servicing of calls and clearance requests (e.g., departure, arrival, or airspace transition) to ATC for the N aircraft. Important parameters for the queueing model are the task arrival rate and the average service time for task completion. Radio communication times from past human-in-the-loop simulation studies are used to measure service times for a 1:4 and 1:12 UAM operation and to interpolate service times for 4 < N < 12. A Monte Carlo method is then employed, using the measured and interpolated service times, to estimate arrival rate and related queueing statistics. The paper concludes by considering the estimated queuing statistics, particularly the RPIC’s utilization (i.e., proportion of time actively servicing tasks), the length of the task queue over time, and the implications for task-balanced system design.

task load↗
DOE OSTI2021

Adapting Existing Energy Models for Resilience Analysis

Resilience is a topic receiving much attention in relation to energy systems, with particular attention being paid to the supply of electricity. As a result of the growing interest in energy sector resilience, research communities have proposed a plethora of candidate resilience indicators and metrics, most of which remain immature at different scales and segments within the energy system. A necessary focus of the research community lies in implementing, testing, and validating resilience metrics and analysis approaches in energy sector models, which will be invaluable for informing resilience planning and investment decisions. Recognizing these challenges that need to be addressed, we explore how to effectively integrate resilience considerations into energy sector models and tools. The overarching goal of the effort was to evaluate the data needs, methodologies, and outcomes—including consequences and/or changes in investment or operational decisions due to avoided consequences—based on resilience analysis in a range of existing tools. In particular, we selected five models originally built at NREL to explore non-resilience energy research questions to implement and exercise resilience metrics and analysis approaches. To demonstrate the importance of perspective, we selected models that represent different segments of the energy sector, geographic scales, and modeling approaches. A second important aspect of our effort was the development of generalized power interruption scenarios. These scenarios were intended to help establish a framework for simulating the effects of real-world threats in terms of their impacts on system components and, in turn, power interruption.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Evaluating and Countering the Insider Threat to the Radioactive Source Supply Chain

The modern supply chain is a global enterprise and little drove this home more than the global COVID-19 pandemic which sent economic shockwaves throughout the world. Many goods became scarce, as products were delayed, in limited supply, or simply not available. The suddenly diminished supply collided with still high demand and led to greatly increased costs. This was particularly true for the radioactive source supply chain. The pandemic introduced extensive delays for construction projects, slowed the transport of radiological materials to facilities, interrupted treatment deliveries, and impaired the mobility of contractors across the industry. All of these concerns not only adversely affected the economy, but also impacted the safety and security of radiological material, potentially raising national security concerns. The vulnerability of the supply chain, a critical element in an increasingly interconnected world, was exposed. One example that challenged the adaptive capacity of the overall supply chain is the Ever Given container ship, which became stuck in the Suez Canal in 2020. This accident immediately shut down shipments that accounted for 12% of global trade, with long-term impacts estimated to be much larger. Developing the ability to anticipate and react in real-time to sudden changes has quickly become a necessity, particularly in industries that deal with the transport of hazardous material. The reaction to these dramatic incidents was to largely focus attention and resources on protecting the supply chain from external threats. However, the threat to the radioactive material supply chain from insiders intimately involved in the process may be even greater and remains a blind spot that requires increased attention. Recent events revealed the blueprint for targeting and disrupting that supply chain, so the potential for a malicious insider—or a manipulated, unwitting insider—to take advantage of this vulnerability is elevated, creating security concerns for radiological industries. This paper examines and analyzes the potential insider threat to the radioactive source supply chain and recommends steps to take to counter this possibility.

Kinney, Justin↗
DOE OSTI2023

Impact of the Electronic Properties of Chalcogenide Ligands in their Complexation with Uranyl Nitrate Complexes

The idea of covalency being directly correlated with selectivity in f-element separations has gained attention over the years. However, “covalency” as a concept is still under debate as it is not an experimental nor quantum mechanical observable. Regardless, covalency along with ionicity are still the main concepts used to explain the nature of the chemical bond of coordination complexes. Another concept that has gained the attention in the separations community is the hard-soft acid-base (HSAB) theory, which favors soft-donor ligands having the potential to increase selectivity for actinides (An) over the lanthanides (Ln) due to their ability to engage more effectively f-electrons in covalent interactions. However, it has been shown that while using softer donor ligands in fact increased orbital mixing and covalency with the actinides, it compromises the strength of the interaction owing to a decrease in the metal-ligand electrostatic interactions.1 Herein, we study the electronic structure of different types of chalcogenide ligands (LE, E = O, S, Se), their interaction with the uranyl ion, and their complexation free energies. Surprisingly, contrary to the expected order of covalency, i.e. An–LSe > An–LS > An–LO, our results suggest that the degree of covalency and associated complexation free energies depend on the nature of the coordinating moiety rather than solely on the directly coordinated atom. 1) Sadhu, Biswajit, and Michael Dolg. Enhancing actinide (III) over lanthanide (III) selectivity through hard-by-soft donor substitution: exploitation and implication of near-degeneracy-driven covalency. Inorganic Chemistry, 2019, 58, 9738 – 9748.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Towards Content Authenticity: Multimodal Fake News Detection and AI-Generated Text Identification

In today’s digital world, the spread of fake news and the rise of AI-generated text have become major threats to content authenticity and public trust. This thesis addresses both challenges through two complementary research directions: detecting fake news using multimodal features, and identifying AI-generated text using semantic and structural reasoning. The first part of the work focuses on fake news detection by introducing a novel model that combines text and image features through a unique rotational attention mechanism. Unlike traditional attention methods, this approach rotates the roles of query, key, and value across modalities to capture deeper interactions. Additionally, the model incorporates external domain information by linking news posts to top-ranked websites from Google search results, which helps assess the credibility of content based on its broader web context. This results in a more reliable and accurate fake news detection system that outperforms existing state-of-the-art methods. The second part presents SGG-ATD, a new framework for detecting AI-generated text. It uses masked language modeling to measure sentence coherence, followed by constructing a graph where keywords—both original and predicted—are connected based on semantic and contextual similarity. A Graph Convolutional Network (GCN) is then used to learn structural relationships within the text for final classification. Experimental results demonstrate that SGG-ATD achieves high F1-scores and consistently outperforms strong baselines. This method contributes to robust AI text detection, supporting accountability and resilience against AI-driven misinformation.

Gupta, Nidhi↗
DOE OSTI2025

SHF: Symmetrical Hierarchical Forest with Pretrained Vision Transformer Encoder for High-Resolution Medical Segmentation

This paper presents a novel approach to addressing the long-sequence problem in high-resolution medical images for Vision Transformers (ViTs). Using smaller patches as tokens can enhance ViT performance, but quadratically increases computation and memory requirements. Therefore, the common practice for applying ViTs to high-resolution images is either to: (a) employ complex sub-quadratic attention schemes or (b) use large to medium-sized patches and rely on additional mechanisms within the model to capture the spatial hierarchy of details. We propose Symmetrical Hierarchical Forest (SHF), a lightweight approach that adaptively patches the input image to increase token information density and encode hierarchical spatial structures into the input embedding. We then apply a reverse depatching scheme to the output embeddings of the transformer encoder, eliminating the need for convolution-based decoders. Unlike previous methods that modify attention mechanisms or use a complex hierarchy of interacting models, SHF can be retrofitted to any ViT model to allow it to learn the hierarchical structure of details in high-resolution images without requiring architectural changes. Experimental results demonstrate significant gains in computational efficiency and performance: on the PAIP WSI dataset, we achieved a 3∼32×speedup or a 2.95%∼7.03% increase in accuracy (measured by Dice score) at a 64K2 resolution with the same computational budget, compared to state-of-the-art production models. On the 3D medical datasets BTCV and KiTS, training was 6×faster, with accuracy gains of 6.93% and 5.9%, respectively, compared to models without SHF.

Zhang, Enzhi [Hokkaido University, Japan]↗
DOE OSTI2025

Sub-microsecond Transformers for Jet Tagging on FPGAs

We present the first sub-microsecond transformer implementation on an FPGA achieving competitive performance for state-of-the-art high-energy physics benchmarks. Transformers have shown exceptional performance on multiple tasks in modern machine learning applications, including jet tagging at the CERN Large Hadron Collider (LHC). However, their computational complexity prohibits use in real-time applications, such as the hardware trigger system of the collider experiments up until now. In this work, we demonstrate the first application of transformers for jet tagging on FPGAs, achieving $\mathcal{O}(100)$ nanosecond latency with superior performance compared to alternative baseline models. We leverage high-granularity quantization and distributed arithmetic optimization to fit the entire transformer model on a single FPGA, achieving the required throughput and latency. Furthermore, we add multi-head attention and linear attention support to hls4ml, making our work accessible to the broader fast machine learning community. This work advances the next-generation trigger systems for the High Luminosity LHC, enabling the use of transformers for real-time applications in high-energy physics and beyond.

Laatu, Lauri [Imperial Coll., London]↗