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At least 307 records · Page 17

Inferring the palaeoenvironment of ancient bacteria on the basis of resurrected proteins

Features of the physical environment surrounding an ancestral organism can be inferred by reconstructing sequences of ancient proteins made by those organisms, resurrecting these proteins in the laboratory, and measuring their properties. Here, we resurrect candidate sequences for elongation factors of the Tu family (EF-Tu) found at ancient nodes in the bacterial evolutionary tree, and measure their activities as a function of temperature. The ancient EF-Tu proteins have temperature optima of 55-65 degrees C. This value seems to be robust with respect to uncertainties in the ancestral reconstruction. This suggests that the ancient bacteria that hosted these particular genes were thermophiles, and neither hyperthermophiles nor mesophiles. This conclusion can be compared and contrasted with inferences drawn from an analysis of the lengths of branches in trees joining proteins from contemporary bacteria, the distribution of thermophily in derived bacterial lineages, the inferred G + C content of ancient ribosomal RNA, and the geological record combined with assumptions concerning molecular clocks. The study illustrates the use of experimental palaeobiochemistry and assumptions about deep phylogenetic relationships between bacteria to explore the character of ancient life.

Bacteria/classification/metabolism↗

Aeroacoustic Flow Phenomena Accurately Captured by New Computational Fluid Dynamics Method

One of the challenges in the computational fluid dynamics area is the accurate calculation of aeroacoustic phenomena, especially in the presence of shock waves. One such phenomenon is "transonic resonance," where an unsteady shock wave at the throat of a convergent-divergent nozzle results in the emission of acoustic tones. The space-time Conservation-Element and Solution-Element (CE/SE) method developed at the NASA Glenn Research Center can faithfully capture the shock waves, their unsteady motion, and the generated acoustic tones. The CE/SE method is a revolutionary new approach to the numerical modeling of physical phenomena where features with steep gradients (e.g., shock waves, phase transition, etc.) must coexist with those having weaker variations. The CE/SE method does not require the complex interpolation procedures (that allow for the possibility of a shock between grid cells) used by many other methods to transfer information between grid cells. These interpolation procedures can add too much numerical dissipation to the solution process. Thus, while shocks are resolved, weaker waves, such as acoustic waves, are washed out.

Blech, Richard A.↗

Exact Cavity Perturbation Technique to Determine Complex Permittivity of Dielectric Materials

Cassini is an international spacecraft mission facilitated by NASA and ESA which seeks to understand the Saturn planetary system, including rings and moons. Launched in 1997, the Cassini spacecraft contains two major components: the Cassini orbiter that has been orbiting Saturn since October 2004, and the European-built Huygens probe that landed on Titan's surface in December 2004 to study its geology and atmosphere. Titan, Saturn's largest moon and the second largest moon in the solar system, possesses surface and atmospheric features similar to those of Earth, including lakes, seas, and mountains. A physical characterization of these features is critical to understanding the origin and evolution of Titan, whose surface composition reflects its geological history. Because Titan's atmosphere is largely composed of methane, it is believed that surfaces lakes are filled with mixtures of liquid hydrocarbons. The Cassini orbiter's RADAR instrument has been scanning Titan's surface at the atmosphere-penetrating microwave frequency of 13.8 Gigahertz since 2004. However, accurate interpretation of these data is limited by a lack of knowledge regarding dielectric properties of liquid hydrocarbons at cryogenic temperatures. Therefore, it is of specific interest to experimentally determine values for the complex permittivities of various liquid hydrocarbon mixtures at the surface conditions of Titan. In particular, more accurate values for complex permittivity would improve estimates of lake depth and surface composition obtained from the instrument's altimetry and backscatter modes.

dielectric constant↗

Recent Developments on the Turbulence Modeling Resource Website (Invited)

The NASA Langley Turbulence Model Resource (TMR) website has been active for over five years. Its main goal of providing a one-stop, easily accessible internet site for up-to-date information on Reynolds-averaged Navier-Stokes turbulence models remains unchanged. In particular, the site strives to provide an easy way for users to verify their own implementations of widely-used turbulence models, and to compare the results from different models for a variety of simple unit problems covering a range of flow physics. Some new features have been recently added to the website. This paper documents the site's features, including recent developments, future plans, and open questions.

Rumssey, Christopher L.↗

The Resilience of Habitable Climates Around Circumbinary Stars

Here we use a 3‐D climate system model to study the habitability of Earth‐like planets orbiting in circumbinary systems. In the most extreme cases, Earth‐like planets in circumbinary systems could experience variations in the incident stellar flux of up to ~50% on ~100‐day timescales. However, we find that Earth‐like planets, having abundant surface liquid water, are generally effective at buffering against these time‐dependent changes in the stellar irradiation due to the high thermal inertia of oceans compared with the relatively short periods of circumbinary‐driven variations in the received stellar flux. Ocean surface temperatures exhibit little to no variation in time; however, land surfaces can experience modest changes in temperature, thus exhibiting an additional mode of climate variability driven by the circumbinary variations. Still, meaningful oscillations in surface temperatures are only found for circumbinary system architectures featuring the largest physically possible amplitudes in the stellar flux variation. In the most extreme cases, an Earth‐like planet could experience circumbinary‐driven variations in the global mean land surface temperature of up to ~5 K, and variations of local daytime maximum temperatures of up to ~12 K on seasonal timescales, while the global mean ocean temperatures vary by less than ~2 K. Such seasonal temperature swings over land areas could potentially pose adaptability challenges for extant life. Still, habitable planets in circumbinary systems appear to be remarkably resilient against circumbinary‐driven climate variations and can avoid any true climate catastrophes.

Eric T Wolf↗

ModuleOT: A Hardware Security Module for Operational Technology: Preprint

With increasing penetration levels of distributed energy resources (DERs) on the distribution grid, as well as new technological advancements in the cyber space, new cyberattack vectors are being introduced, and the available attack surface is constantly increasing. Despite this increasing risk, the standard IEEE 1547-2018 does not yet recommend cybersecurity measures for DERs. To address this and to better protect data on the distribution grid - from the standpoints information security as well as operational security - ModuleOT has been developed. The module aims to significantly reduce cyberattack vectors by improving data privacy for user applications. This is accomplished by performing the core functions of encryption, authentication, authorization, certificate management, and user access control. The module integrates a custom security application with hardware cryptographic acceleration. The application secures all communications using Transmission Control Protocol over Internet Protocol (TCP/IP). These include the three most commonly used communications protocols for power systems information exchange: Modbus, Distributed Network Protocol 3 (DNP3), and Smart Energy Profile 2.0 (SEP2.0). These three protocols are also supported by IEEE 1547-2018 for all DER devices. This paper tests the data encryption/decryption feature on a physical networking test bed with emulated Modbus devices reporting grid data and presents the results.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Transamericium Radiation-Induced Redox Chemistry

The actinide series boasts many unique physical and chemical features worthy of study. However, the chemical influence of their inherent radiation field is often overlooked, especially as we begin to explore the late actinides (transamericium) in more detail than ever possible before. - Absorption of ionizing radiation induces the formation of a variety of transient and steady-state radicals, ions, and molecular degradation products. Many of these radiolysis products are strongly redox active and exhibit significant reactivity with a number of actinides, e.g., neptunium and americium. Here we present results from the first-ever time-resolved picosecond pulsed electron radiolysis measurements for the reaction of the late actinides (curium and californium) with reactive transients pertinent to actinide manipulations in aqueous solution, i.e., the hydrated electron (eaq–), the hydrogen atom (H•), and hydroxyl (•OH) and nitrate (•NO3) radicals.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Physically Informed Machine Learning Prediction of Electronic Density of States

The electronic structure of a material, such as its density of states (DOS), provides key insights into its physical and functional properties and serves as a valuable source of high-quality features for many materials screening and discovery workflows. Still, the computational cost of calculating the DOS, most commonly with density functional theory (DFT), becomes prohibitive for meeting high-fidelity or high-throughput requirements, necessitating a cheaper but sufficiently accurate surrogate. To fulfill this demand, we develop a general machine learning method based on graph neural networks for predicting the DOS purely from atomic positions, six orders of magnitude faster than DFT. This approach can effectively use large materials databases and be applied generally across the entire periodic table to materials classes of arbitrary compositional and structural diversity. We furthermore devise a highly adaptable scheme for physically informed learning which encourages the DOS prediction to favor physically reasonable solutions defined by any set of desired constraints. This functionality provides a means for ensuring that the predicted DOS is reliable enough to be used as an input to downstream materials screening workflows to predict more complex functional properties, which rely on accurate physical features.

36 MATERIALS SCIENCE↗

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗

Use of Landsat-derived temporal profiles for corn-soybean feature extraction and classification

A physical model is presented, which has been derived from multitemporal-multispectral data acquired by Landsat satellites to describe the behavior and new features that are crop specific. A feasibility study over 40 sites was performed to classify the segment pixels into those of corn, soybeans, and others using the new features and a linear classifier. Results agree well with other existing methods, and it is shown the multitemporal-multispectral scanner data can be transformed into two parameters that are closely related to the target of interest and thus can be used in classification. The approach is less time intensive than other techniques and requires labeling of only pure pixels.

Badhwar, G. D.↗

Requirements and Recommendations for a Physical Attack Characterization Framework

This study seeks to identify existing frameworks or develop requirements and recommendations for a new framework that can consistently characterize physical attacks, analogous to MITRE ATT&CK®. MITRE ATT&CK is widely used across government, research organizations, and the cyber security community to characterize cyber attack tactics, techniques, and procedures (TTPs) in a consistent and commonly understood manner. While physical attack taxonomies, methodologies, and other tools for evaluating physical security do exist, many are sector and/or facility-type specific—and therefore not able to provide comparable scenarios across sectors—or are more focused on security assessment instead of the characterization of attacks themselves. A MITRE ATT&CK analog for physical attacks on critical infrastructure would provide a common language and structure for analysis of physical attacks. Existing attack characterization methodologies do not robustly address cyber-physical security risks. To fully understand a facility’s security needs, it is important to understand the entire vulnerability landscape from both a physical and a cyber perspective. To underscore this need, organizations such as the Cybersecurity and Infrastructure Security Agency (CISA) are calling for a coordinated approach to cyber and physical security, which they refer to as cyber and physical security convergence. A physical attack characterization framework that could be used jointly with MITRE ATT&CK would help support a more robust analysis in support of convergence, enabling the consistent characterization of attacks that utilize both cyber and physical tactics and techniques. This could provide analysts and stakeholders with a clearer understanding of how security mitigations deployed in the physical realm impact security risks in the cyber realm, and vice versa. In this study, the project team evaluates existing physical security taxonomies and methodologies to assess whether an existing method can be used to create a “physical half” of MITRE ATT&CK. This study then provides requirements and recommendations for a framework that can leverage aspects of existing methodologies. The goal of the final framework is for it to be widely adopted and referenced, regardless of critical infrastructure sector, facility type, or facility components. This study also identifies applicable use cases for when and how a framework could be applied across the various critical infrastructure sectors for a variety of attack types or motivations. Through a literature review of existing security-focused methodologies and taxonomies, engagement with relative stakeholders, evaluation of potential physical attack framework use cases, and subsequent identification of requirements, this study identified the following key findings and recommendations: There is a need for a new physical attack characterization framework; A physical attack framework should be interoperable with the MITRE ATT&CK framework; A physical attack framework should be broadly applicable, but with detailed tactics, techniques, and procedures that encompass the entire attack path; A physical attack framework should be based on observed or feasible events; A physical attack framework should adapt features from existing methodologies, frameworks, and taxonomies; A physical attack framework should be owned, overseen, and maintained by one organization.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Leveraging generative artificial intelligence to bridge domain gaps in wind turbine research

A central challenge in wind turbine health monitoring is the scarcity of real-world data due to limited instrumentation, leading researchers to rely on simulation models that often suffer from reduced fidelity. However, even within simulation environments, discrepancies arise because of modeling assumptions, and configuration fidelities, creating domain gaps that limit the transferability of learned representations. Here, to investigate domain translation under controlled conditions, this project explores the use of generative artificial intelligence, specifically cycle-consistent generative adversarial networks (CGANs), to bridge the gap between OpenFAST simulation models representing 1.5 MW and 5 MW wind turbines. A physics-informed CGAN architecture is introduced, where a simplified turbine tower dynamics model is incorporated into the training loss to ensure physically consistent outputs. Quantitative results showed moderate to high agreement in frequency-domain features. Incorporating the physics-informed loss function improved the R 2 values by 30%, reduced the RMSE from 1.39 to 1.1 m/s 2 , and reduced training time by 82%. Furthermore, under increased turbulence intensity (IEC Category A), the RMSE remained stable at approximately 1.1 m/s 2 . While the present study is entirely simulation-based, it establishes a pipeline for evaluating physics-informed generative domain translation, which may serve as a foundation for future simulation-to-reality validation studies.

17 WIND ENERGY↗

ISO Spectroscopy of Proto-Planetary Nebulae

The goal of this program was to determine the chemical properties of the dust shells around protoplanetary nebulae (PPNs) through a study of their short-wavelength (6-45 micron) infrared spectra. PPNs are evolved stars in transition from the asymptotic giant branch to the planetary nebula stages. Spectral features in the 10 to 20 gm region indicate the chemical nature (oxygen- or carbon-rich), and the strengths of the features relate to the physical properties of the shells. A few bright carbon-rich PPNs have been observed to show PAH features and an unidentified 21 micron emission feature. We used the Infrared Space Observatory (ISO) to observe a sample of IRAS sources that have the expected properties of PPNs and for which we have accurate positions. Some of these have optical counterparts (proposal SWSPPN01) and some do not (SWSPPN02). We had previously observed these from the ground with near-infrared photometry and, for those with visible counterparts, visible photometry and spectroscopy, which we have combined with these new ISO data in the interpretation of the spectra. We have completed a study of the unidentified emission feature at 21 micron in eight sources. We find the shape of the feature to be the same in all of the sources, with no evidence of any substructure. The ratio of the emission peak to continuum ranges from 0.13 to 1.30. We have completed a study of seven PPNs and two other carbon-rich objects for which we had obtained ISO 2-45 micron observations. The unidentified emission features at 21 and 30 micron were detected in six sources, including four new detections of the 30 micron feature. This previously unresolved 30 micron feature was resolved and found to consist of a broad feature peaking at 27.2 micron (the "30 micron" feature) and a narrower feature peaking at 25.5 micron (the "26 micron" feature). This new 26 micron feature is detected in eight sources and is particularly strong in IRAS Z02229+6208 and 16594-4656. The unidentified features at 3.3, 6,2, 7.7, and 11.3 micron, which are commonly observed in planetary nebulae and HII regions, are also seen in these PPNs. However, their strengths relative to the continuum plateaus at 8 and 12 micron are weaker than in planetary nebulae. The 6.9 micron feature, seen almost exclusively in PPNs, is strong. The spectral energy distributions of these PPNs were fitted with a radiative-transfer model, taking into account the emission features at 21, 26, and 30 micron. A significant fraction of the total energy output is emitted in these features: as high as 20% in the 30 micron feature and 8% in the 21 micron feature. The fact that so much energy is carried in these features suggests that the material responsible for this feature must be made of abundant elements, and most likely involves carbon. The change in the in feature strengths from stronger aliphatic bonds in PPNs to stronger aromatic bonds in PNs suggests a chemical and physical evolution in the carbonaceous circumstellar dust during this transition time scale of a few thousand years.

Hrivnak, Bruce J.↗

A cloud physics radiometer

The paper describes the design features and capabilities of a seven-channel cloud physics radiometer (CPR) for remote sensing of cloud properties. The CPR channel characteristics and functions are tabulated and diagrammed. Each of the first three channels utilizes a photo-multipler detector, with the high-voltage power supply integrated with the tube into a single unit. In operation a heater is used to keep the optics temperature at or above 273 K and this temperature is constantly monitored. The last four channel detectors and filters are all cooled to the temperature of liquid nitrogen. The inclined scanning mirror rotates at a rate of 3.48 rps. Registration pulses are triggered and recorded as the mirror enters and leaves the + or -45 deg earth observation region. The ice-cloud, water cloud, snow discriminator detector has worked quite well in general. Interesting radiometer data have been obtained and their analysis is under way. The combination of the CPR and the Cloud Lidar System will make possible sophisticated remote sensing cloud studies.

Kyle, H. L.↗

The structural information filtered features (SIFF) potential: Maximizing information stored in machine-learning descriptors for materials prediction

Machine learning inspired potentials continue to improve the ability for predicting structures of materials. However, many challenges still exist, particularly when calculating structures of disordered systems. These challenges are primarily due to the rapidly increasing dimensionality of the feature-vector space which in most machine-learning algorithms is dependent on the size of the structure. In this article, we present a feature-engineered approach that establishes a set of principles for representing potentials of physical structures (crystals, molecules, and clusters) in a feature space rather than a physically motivated space. Our goal in this work is to define guiding principles that optimize information storage of the physical parameters within the feature representations. In this manner, we focus on keeping the dimensionality of the feature space independent of the number of atoms in the structure. Our Structural Information Filtered Features (SIFF) potential represents structures by utilizing a feature vector of low-correlated descriptors, which correspondingly maximizes information within the descriptor. We present results of our SIFF potential on datasets composed of disordered (carbon and carbon–oxygen) clusters, molecules with C 7 O 2 H 2 stoichiometry in the GDB9-14B dataset, and crystal structures of the form (Al x Ga y In z ) 2 O 3 as proposed in the NOMAD Kaggle competition. Our potential's performance is at least comparable, sometimes significantly more accurate, and often more efficient than other well-known machine-learning potentials for structure prediction. However, primarily, we offer a different perspective on how researchers should consider opportunities in maximizing information storage for features.

36 MATERIALS SCIENCE↗

Integrating AI with physics-based hydrological models and observations for insightinto changing climate and anthropogenic impacts

Focal Areas: Advanced computational methods that integrate AI, physics, and observations to provide predictive landscape hydrological modeling over large areas (regional, continental, worldwide) while incorporating increasingly available high-resolution data from drones, lidar and satellite. Science Challenge: Landscape data is available at finer scales than can be used in physics-based hydrological (PBH) models for regional or continental terrestrial water modeling. Thus, we throw away observable detail to achieve computability. We argue that integration of AI with PBH models and observed data can be used to provide upscaling for predictive models that are computable, retain physical conservation properties, and represent the fine-scale features that affect complex flow physics through both natural and urban environments. Developing such next-generation capabilities requires outside-the-box thinking that melds the different approaches of AI modeling, PBH modeling, and observation across multiple scales from local drones to satellites.

54 ENVIRONMENTAL SCIENCES↗

Nuclear masses learned from a probabilistic neural network

Machine learning methods and uncertainty quantification have been gaining interest throughout the last several years in low-energy nuclear physics. In particular, Gaussian processes and Bayesian neural networks have increasingly been applied to improve mass model predictions while providing well-quantified uncertainties. In this work, we use the probabilistic Mixture Density Network (MDN) to directly predict the mass excess of the 2016 Atomic Mass Evaluation within the range of measured data, and we extrapolate the inferred models beyond available experimental data. The MDN provides not only mean values but also full posterior distributions both within the training set and extrapolated testing set. We show that the addition of physical information to the feature space increases the accuracy of the match to the training data as well as provides for more physically meaningful extrapolations beyond the the limits of experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Near-Infrared Spectra of Type Ia Supernovae

We report near-infrared (NIR) spectroscopic observations of 12 'branch-normal' Type Ia supernovae (SNe Ia) that cover the wavelength region from 0.8 to 2.5 microns. Our sample more than doubles the number of SNe Ia with published NIR spectra within 3 weeks of maximum light. The epochs of observation range from 13 days before maximum light to 18 days after maximum light. A detailed model for a Type Ia supernovae is used to identify spectral features. The Doppler shifts of lines are measured to obtain the velocity and thus the radial distribution of elements. The NIR is an extremely useful tool to probe the chemical structure in the layers of SNe Ia ejecta. This wavelength region is optimal for examining certain products of the SNe Ia explosion that may be blended or obscured in other spectral regions. We identify spectral features from Mg II, Ca II, Si II, Fe II, Co II, Ni II, and possibly Mn II. We find no indications for hydrogen, helium, or carbon in the spectra. The spectral features reveal important clues about the physical characteristics of SNe Ia. We use the features to derive upper limits for the amount of unburned matter, to identify the transition regions from explosive carbon to oxygen burning and from partial to complete silicon burning, and to estimate the level of mixing during and after the explosion. Elements synthesized in the outer layers during the explosion appear to remain in distinct layers. That provides strong evidence for the presence of a detonation phase during the explosion as it occurs in delayed detonation or merger models. Mg II velocities are found to exceed 11,000 - 15,000 km/s, depending on the individual SNe Ia. That result suggests that burning during the explosion reaches the outermost layers of the progenitor and limits the amount of unburned material to less than 10% of the mass of the progenitor. Small residuals of unburned material are predicted by delayed detonation models but are inconsistent with pure deflagration or merger models. Differences in the spectra of the individual SNe Ia demonstrate the variety of these events.

Marion, G. H.↗