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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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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Domain Adaptation for Convolutional Neural Networks Based Remote Sensing Scene Classification

Remote sensing (RS) scene classification plays an important role in the field of earth observation. With the rapid development of the remote sensing techniques, a large number of RS scene images are available. Since manually labeling largescale RS scene images is both labor and time consuming, when a new unlabeled dataset is obtained, how to use the existing labeled datasets to classify the new unlabeled images is an important research direction. Different RS scene image datasets may be taken from different type of sensors, and the images may vary from imaging modalities, spatial resolutions and image scales, so the distribution discrepancy exists among different image datasets. As a result, simply applying Convolutional Neural Networks (CNN) trained on source domain cannot accurately classify the images on target domain. Domain Adaptation (DA) can be helpful to solve this problem. In this paper, we design a Subspace Alignment (SA) and CNN based framework to solve the DA problem in RS scene image classification. A new SA layer is proposed and added into CNN models for DA, which could align the source and target domains in some feature subspace. Finetuning the modified CNN model with the added SA layer is able to make the CNN model adapt to the aligned feature subspace and helps to relieve the domain distribution discrepancy. The experiments conducted on two public datasets show that adding the SA layer into CNN can improve the scene classification on target domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

From hydrogen bonding to metal coordination and back: Porphyrin-based networks on Ag(111)

The self-assembly of a metal-free porphyrin bearing two pyridyl coordinating sites and two pentyl chains at trans meso positions was investigated under ultrahigh vacuum on a Ag(111) surface by scanning tunneling microscopy (STM). The STM measurements revealed a well-ordered close-packed structure with a rhombic unit cell for coverages ≤1 monolayer with their molecular plane parallel to the surface. The growth direction of the molecular islands is aligned along the step edges, which are restructured due to molecule-substrate interactions. The shorter unit cell vector of the molecular superstructure follows the 〈1-10〉 direction of the Ag(111) substrate. Hydrogen bonds between pyridyl and pyrrole groups of neighboring molecules as well as weak van der Waals forces between the pentyl chains stabilize the superstructure. Deposition of cobalt atoms onto the close-packed structure at room temperature leads to the formation of a hexagonal porous network stabilized by metal-ligand bonding between the pyridyl ligands and the cobalt atoms. Thermal annealing of the Co-coordination network at temperatures >450 K results in the transformation of the hexagonal network into a second close-packed structure. Changes in the molecule-substrate interactions due to metalation of the porphyrin core with Co as well as intermolecular interactions can explain the observed structural transformations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Contrasting Public Opinion Dynamics and Emotional Response during Crisis

We propose an approach for contrasting spatiotemporal dynamics of public opinions expressed toward targeted entities, also known as stance detection task, in Russia and Ukraine during crisis. Our analysis relies on a novel corpus constructed from posts on the VKontakte social network, centered on local public opinion of the ongoing Russian-Ukrainian crisis, along with newly annotated resources for predicting expressions of fine-grained emotions including joy, sadness, disgust, anger, surprise and fear. Akin to prior work on sentiment analysis we align traditional public opinion polls with aggregated automatic predictions of sentiments for contrastive geo-locations. We report interesting observations on emotional response and stance variations across geo-locations. Some of our findings contradict stereotypical misconceptions imposed by media, for example, we found posts from Ukraine that do not support Euromaidan but support Putin, and posts from Russia that are against Putin but in favor USA. Furthermore, we are the first to demonstrate contrastive stance variations over time across geo-locations using storyline visualization technique.

emotion analysis↗
DOE OSTI2026

Dynamic Interfacial Design in Adaptive Hybrid Materials Enables Reversible and Tunable Mechano-Optic Smart Responses

Next-generation polymeric materials are shifting toward adaptive and interactive behaviors of living systems; however, designing materials that can reversibly modulate optical properties under mechanical deformation while maintaining mechanical robustness remains a key challenge. Here, we report a mechanically robust vitrimer-based adaptive hybrid material (AHM) that exhibits a stretch-induced reversible transparency-to-opacity transition, enabled by the integration of dynamic interactions at the polymer–silica nanoparticle interface and controlled nanoparticle self-assembly. The AHM combines boronic ester–functionalized polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (S-Bpin) with diol-functionalized silica nanoparticles (diol-SiNPs) to form a hybrid network hosting both dynamic boronic ester and hydrogen-bonding interactions. These reversible linkages facilitate controlled nanoparticle self-assembly and enable strain-induced nanoparticle alignment/aggregation. Upon stretching, SiNP-rich domains align and aggregate within the polymer matrix, while local modulus mismatch between stiff aggregated SiNP/borylated-styrene-rich regions and the softer elastomeric midblock induces surface microwrinkle formation. These internal aggregates and surface wrinkles cooperatively enhance light scattering, producing the opaque state under strain. Furthermore, the tailored AHM exhibits high toughness, thermomechanical stability, reprocessability, and programmable shape-memory behavior. This work presents a dynamic interfacial design strategy for mechanically robust, optically reconfigurable, and reusable soft materials for adaptive optics, smart windows, sensing, soft robotics, and circular smart-material platforms.

adaptive hybrid materials↗
DOE OSTI2024

Environmental Molecular Network (ENVnet) v1

Here, we present an approach that integrates mass difference based deconvolution with molecular networking to build a static reference network from all publicly available organic matter metabolomics datasets. This is accomplished using MS/MS deconvolution coupled with both recently reported (BUDDY) and novel machine learning algorithms to determine chemical formulas and perform MS/MS alignments (REM-BLINK).

Bowen, Benjamin↗
DOE OSTI2026

Accelerating Next-Generation Cybersecurity R&D Using AI Workflows: BADGER Project Development

The Broadband Automation for Distributed Grid Efficiency and Resilience (BADGER) project aligns with national strategic priorities for integrating emerging wireless technologies and advancing AI-driven security. As critical infrastructure modernizes toward increasingly software-defined and interconnected systems, the ability to leverage 5G/NextG networks and AI-enabled control becomes essential. This report outlines work at the National Laboratory of the Rockies (NLR) to develop a NextG-native security architecture powered by AI-RAN concepts and evaluate workflows that enable efficient and reliable architectures. Together, these efforts position the laboratory to accelerate innovation while directly supporting national security and resilience objectives.

5G/6G↗
DOE OSTI2022

Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other dataset. Here we develop a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain alignment that can be applied to datasets with different types of class overlap. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS). We show that our method is capable of bridging the gap between two astronomical surveys, and also performs well for anomaly detection and clustering of unknown data in the unlabeled dataset. We apply our model to two examples of galaxy morphology classification tasks with anomaly detection: 1) classifying spiral and elliptical galaxies with detection of merging galaxies (three classes including one unknown anomaly class); 2) a more granular problem where the classes describe more detailed morphological properties of galaxies, with the detection of gravitational lenses (ten classes including one unknown anomaly class).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Three-Dimensional Grid Visualization for Planning Activities: A Dubai Case Study

National Laboratory of the Rockies (NLR), in collaboration with the Dubai Electricity and Water Authority (DEWA) and Infra-X, has undertaken the Energy Visualization Analysis Project. The aim of this project is to enhance analytical and 3D visualization capabilities for distribution network planning and renewable energy integration. As modern grid continues to evolve with large-scale solar PV deployment and emerging distributed energy resources (DERs), the ability to effectively analyze, visualize, and communicate complex grid behaviors has become increasingly critical. The project focuses on developing empirical use cases based on real distribution feeder data and engineering workflows, ensuring the outcomes are directly aligned with operational environment. Through time-series power flow simulations and nodal hosting capacity analysis, the study quantifies the impacts of high PV penetration on voltage and thermal limits within representative 11 kV feeders. These analyses identify specific nodes and conditions where DER integration challenges arise. Furthermore, a Battery Energy Storage System (BESS) optimization algorithm was applied to determine the optimal size and placement of storage systems that can mitigate network constraints and enhance hosting capacity. The comparative results between base-case and BESS-augmented scenarios clearly demonstrate improvements in network stability and load management efficiency. In parallel, the NLR team developed an immersive 3D visualization framework, enabling interactive exploration of grid simulations using commodity head-mounted display (HMD) systems. This framework transforms conventional 2D simulation data into spatially intuitive visual environments - allowing engineers to analyze feeder conditions, PV hosting potential, and BESS effects in real time. This report represents the first foundational phase in establishing a visualization-driven analytical ecosystem. It provides a methodological foundation for data integration, visualization architecture, and simulation-based decision support, paving the way for large-scale adoption of immersive visualization across DEWA's Smart Grid Initiative, R&D activities, and future network resilience studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Broadband Infrared Carbon Nanotube Linear Photodetector Arrays

Carbon nanotubes (CNTs) possess exceptional optoelectronic properties, including broadband absorption and high absorption coefficients, making them promising candidates for photodetector applications. However, conventional designs often rely on single or aligned nanotubes, which restrict device scalability and require complex fabrication techniques. Here, in this work, we address these limitations by utilizing CNT-suspended solutions to fabricate large-area photodetectors based on CNT networks via vacuum filtration and direct laser scribing. CNT networks are uniformly formed onto low-thermal-conductance polymeric filter papers that serve as substrates and mitigate the need for suspended structures, reducing the fabrication complexity and cost. The CNT films are patterned into single devices and 49-pixel linear arrays by using a laser-cutting process, enabling scalable and cost-effective production. The resulting devices operate as bolometers and exhibit broad spectral sensitivity extending from visible to midwave infrared, showing stable operation at room temperature. We further demonstrate proof-of-concept IR imaging using linear arrays.

IR imaging↗
DOE OSTI2020

Improving Energy Transfer within Metal–Organic Frameworks by Aligning Linker Transition Dipoles along the Framework Axis

Crystalline metal-organic frameworks (MOFs) can assemble chromophoric molecules into a wide range of spatial arrangements, which are controlled by the MOF topology. Like natural light-harvesting complexes (LHCs), the precise arrangement modulates interchromophoric interactions, in turn determining excitonic behavior and migration dynamics. To unveil the key factors that control efficient exciton displacements within MOFs, we first developed linkers with low electronic symmetry (as defined by large transition dipoles) and then assembled them into MOFs. These linkers possess extended conjugation along one molecular axis, engendering low optical bandgaps and improved oscillator strength for their lowest-energy transition (S 0 → S 1 ). This enhances absorption-emission spectral overlap and boosts the efficiency of Forster resonance energy transfer, which was observed experimentally by a sizable decrease in emission quantum yield (QY), accompanied by a faster population decay profile. We find that MOFs that orient these elongated linkers along their asymmetric pore channel, e.g., the hexagonal pores in an xly network, manifested >50% decrease in their emission QY with faster decay profiles relative to their corresponding solution dissolved linkers. This is due to an efficient migration of photogenerated excitons at the crystallite peripheral sites to internal sites, which was facilitated by polarized absorption-emission overlap among the parallelly aligned linkers. In contrast, symmetric MOFs, such as those with sqc-a topological net, orient elongated linkers along two perpendicular crystal axes, which hinders efficient exciton migration. This study underscores that MOFs are promising to develop artificial LHCs, but that to achieve an efficient exciton displacement, appropriate topology-guided assembly is required to fully realize the true potential of linkers with low electronic symmetry.

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

SIDDA: SInkhorn Dynamic Domain Adaptation

Modern neural networks (NNs) often do not generalize well in the presence of a "covariate shift"; that is, in situations where the training and test data distributions differ, but the conditional distribution of classification labels remains unchanged. In such cases, NN generalization can be reduced to a problem of learning more domain-invariant features. Domain adaptation (DA) methods include a range of techniques aimed at achieving this; however, these methods have struggled with the need for extensive hyperparameter tuning, which then incurs significant computational costs. In this work, we introduce SIDDA, an out-of-the-box DA training algorithm built upon the Sinkhorn divergence, that can achieve effective domain alignment with minimal hyperparameter tuning and computational overhead. We demonstrate the efficacy of our method on multiple simulated and real datasets of varying complexity, including simple shapes, handwritten digits, and real astronomical observations. SIDDA is compatible with a variety of NN architectures, and it works particularly well in improving classification accuracy and model calibration when paired with equivariant neural networks (ENNs). We find that SIDDA enhances the generalization capabilities of NNs, achieving up to a ≈40% improvement in classification accuracy on unlabeled target data. We also study the efficacy of DA on ENNs with respect to the varying group orders of the dihedral group DN, and find that the model performance improves as the degree of equivariance increases. Finally, we find that SIDDA enhances model calibration on both source and target data--achieving over an order of magnitude improvement in the ECE and Brier score. SIDDA's versatility, combined with its automated approach to domain alignment, has the potential to advance multi-dataset studies by enabling the development of highly generalizable models.

Pandya, Sneh [Northeastern U.]↗
DOE OSTI2011

In Silico Analysis of Transcription Factor Repertoires and Prediction of Stress-Responsive Transcription Factors from Six Major Gramineae Plants

The interactions between transcription factors (TFs) and cis-regulatory DNA sequences control gene expression, constituting the essential functional linkages of gene regulatory networks. The aim of this study is to identify and integrate all putative TFs from six grass species: Brachypodium distachyon, maize, rice, sorghum, barley, and wheat with significant information into an integrative database (GramineaeTFDB) for comparative genomics and functional genomics. For each TF, sequence features, promoter regions, domain alignments, GO assignment, FL-cDNA information, if available, and cross-references to various public databases and genetic resources are provided. Additionally, GramineaeTFDB possesses a tool which aids the users to search for putative cis-elements located in the promoter regions of TFs and predict the functions of the TFs using cis-element-based functional prediction approach. We also supplied hyperlinks to expression profiles of those TF genes of maize, rice, and barley, for which data are available. Furthermore, information about the availability of FOX and Ds mutant lines for rice and maize TFs, respectively, are also accessible through hyperlinks. Our study provides an important user-friendly public resource for functional analyses and comparative genomics of grass TFs, and understanding of the architecture of transcriptional regulatory networks and evolution of the TFs in agriculturally important cereal crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Real-time imaging of self-organization and mechanical competition in carbon nanotube forest growth [Real-time environmental TEM investigation of self-organization and mechanical competition in carbon nanotube forest growth]

Here, the properties of carbon nanotube (CNT) networks and analogous materials comprising filamentary nanostructures are governed by the intrinsic filament properties and their hierarchical organization and interconnection. As a result, direct knowledge of the collective dynamics of CNT synthesis and self-organization is essential to engineering improved CNT materials for applications such as membranes and thermal interfaces. Here, we use real-time environmental transmission electron microscopy (E-TEM) to observe nucleation and self-organization of CNTs into vertically aligned forests. Upon introduction of the carbon source, we observe a large scatter in the onset of nucleation of individual CNTs and the ensuing growth rates. Experiments performed at different temperatures and catalyst particle densities show the critical role of CNT density on the dynamics of self-organization; low-density CNT nucleation results in the CNTs becoming pinned to the substrate and forming random networks, whereas higher density CNT nucleation results in self-organization of the CNTs into bundles that are oriented perpendicular to the substrate. We also find that mechanical coupling between growing CNTs alters their growth trajectory and shape, causing significant deformations, buckling, and defects in the CNT walls. Therefore, it appears that CNT–CNT coupling not only is critical for self-organization but also directly influences CNT quality and likely the resulting properties of the forest. As a result, our findings show that control of the time-distributed kinetics of CNT nucleation and bundle formation are critical to manufacturing well-organized CNT assemblies and that E-TEM can be a powerful tool to investigate the mesoscale dynamics of CNT networks.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Classification of bacterial plasmid and chromosome derived sequences using machine learning

Plasmids are important genetic elements that facilitate horizonal gene transfer between bacteria and contribute to the spread of virulence and antimicrobial resistance. Most bacterial genome sequences in the public archives exist in draft form with many contigs, making it difficult to determine if a contig is of chromosomal or plasmid origin. Using a training set of contigs comprising 10,584 chromosomes and 10,654 plasmids from the PATRIC database, we evaluated several machine learning models including random forest, logistic regression, XGBoost, and a neural network for their ability to classify chromosomal and plasmid sequences using nucleotide k-mers as features. Based on the methods tested, a neural network model that used nucleotide 6-mers as features that was trained on randomly selected chromosomal and plasmid subsequences 5kb in length achieved the best performance, outperforming existing out-of-the-box methods, with an average accuracy of 89.38% ± 2.16% over a 10-fold cross validation. The model accuracy can be improved to 92.08% by using a voting strategy when classifying holdout sequences. In both plasmids and chromosomes, subsequences encoding functions involved in horizontal gene transfer—including hypothetical proteins, transporters, phage, mobile elements, and CRISPR elements—were most likely to be misclassified by the model. This study provides a straightforward approach for identifying plasmid-encoding sequences in short read assemblies without the need for sequence alignment-based tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Abstract Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

UNVEILING A NETWORK OF PARALLEL FILAMENTS IN THE INFRARED DARK CLOUD G14.225-0.506

We present the results of combined NH{sub 3} (1,1) and (2,2) line emission observed with the Very Large Array and the Effelsberg 100 m telescope of the infrared dark cloud G14.225-0.506. The NH{sub 3} emission reveals a network of filaments constituting two hub-filament systems. Hubs are associated with gas of rotational temperature T{sub rot} {approx} 15 K, non-thermal velocity dispersion {sigma}{sub NT} {approx} 1 km s{sup -1}, and exhibit signs of star formation, while filaments appear to be more quiescent (T{sub rot} {approx} 11 K and {sigma}{sub NT} {approx} 0.6 km s{sup -1}). Filaments are parallel in projection and distributed mainly along two directions, at P.A. {approx} 10 Degree-Sign and 60 Degree-Sign , and appear to be coherent in velocity. The averaged projected separation between adjacent filaments is between 0.5 pc and 1 pc, and the mean width of filaments is 0.12 pc. Cores within filaments are separated by {approx}0.33 {+-} 0.09 pc, which is consistent with the predicted fragmentation of an isothermal gas cylinder due to the {sup s}ausage{sup -}type instability. The network of parallel filaments observed in G14.225-0.506 is consistent with the gravitational instability of a thin gas layer threaded by magnetic fields. Overall, our data suggest that magnetic fields might play an important role in the alignment of filaments, and polarization measurements in the entire cloud would lend further support to this scenario.

79 ASTRONOMY AND ASTROPHYSICS↗