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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Spontaneous rearrangement of acetylated xylan on hydrophilic cellulose surfaces

The interaction of xylan, an abundant plant polysaccharide, with cellulose microfibrils is essential for secondary cell wall strength. A deeper understanding of these interactions is crucial both to improve our understanding of plant cell wall architecture and to design alternate strategies to overcome cellulose recalcitrance for the production of biofuels and sustainable biomaterials. Naturally occurring acetate or glucuronic acid substitutions on xylan have been shown to influence xylan-cellulose interactions. In this work, we use unrestrained molecular dynamics simulations to determine the interactions with the (110) hydrophilic face of cellulose fibers of four different xylans. In the absence of cellulose, all xylans, independent of the substitution pattern, adopt a highly flexible threefold helical screw conformation. However, when xylan is spatially close to a cellulose surface 1,2 linked acetyl xylans (2AcX) adopt rigid twofold helical screw conformations. The 2AcX conformations are primarily stabilized by interactions between the acetylated oxygen and the glycosidic linkage with C-O6 of cellulose. In contrast, the glycosidic oxygens and acetyl decorations for 1,3 linked acetyl groups (3AcX) are oriented away from the cellulose surface and the 3AcX xylans maintain threefold helical screw conformations on the cellulose surface. Our results show that evenly spaced chemical functionalization (with acetyl groups) and the position of substitution (1,2) on xylan backbone play key roles in tuning the xylan-cellulose interactions to stabilize the twofold helical screw conformations of xylan on the cellulose surface. A comparison with previous experimental findings further suggests that 1,2 substitutions induce twofold helical screw conformations of xylan on the cellulose surface irrespective of the chemical nature of the substituent, while 1,3 substitutions primarily bind lignin in threefold helical screw conformations rather than cellulose in plant cell walls.

1,2 Ac↗
NASA NTRS1992

Recognizing Patterns In Log-Polar Coordinates

Log-Hough transform is basis of improved method for recognition of patterns - particularly, straight lines - in noisy images. Takes advantage of rotational and scale invariance of mapping from Cartesian to log-polar coordinates, and offers economy of representation and computation. Unification of iconic and Hough domains simplifies computations in recognition and eliminates erroneous quantization of slopes attributable to finite spacing of Cartesian coordinate grid of classical Hough transform. Equally efficient recognizing curves. Log-Hough transform more amenable to massively parallel computing architectures than traditional Cartesian Hough transform. "In-place" nature makes it possible to apply local pixel-neighborhood processing.

Weiman, Carl F. R.↗
NASA NTRS2001

TT and C - First TDRSS, Then Commercial GEO and Big LEO and Now through LEO

The advent of low earth orbit (LEO) commercial communications satellites provides an opportunity to dramatically reduce Telemetry Tracking and Control (TT&C) costs of launch vehicles and Unpiloted Aerial Vehicles (UAVs) by reducing or eliminating ground infrastructure. Personnel from the Goddard Space Flight Center Wallops Flight Facility (GSFC/WFF) in Virginia have successfully used commercial GEO & Big LEO communications satellites for Long Duration Balloon flight TT&C. In addition, TDRSS capability for these balloons has been developed by WFF for the Ultra Long Duration Balloons with the first test flight launch in January 2001 for one global circumnavigation at 120,000 feet altitude launched from Alice Springs. Australia. Numerous other low cost applications can new utilize the commercial LEO satellites for TT&C. The Flight Modern became a GSFC/WFF Advanced Range Technology Initiative (ARTI) in an effort to streamline TT&C capability to the user community at low cost. Phase I ground tests of The Flight Modem verified downlink communications quality of service and measured transmission latencies. These tests were completed last year, Phase II consisting of aircraft flight tests provide much of the data presented in this paper. Phase III of the Flight Modern baseline test program is a demonstration of the ruggedized version of the WFF Flight Modem flown on one sounding rocket launched from Sweden. Flights of opportunity have been and are being actively pursued with other centers, ranges and users at universities. The WFF goal is to reduce TT&C costs by providing a low cost COTS Flight Modem with a User Handbook containing system capability and limitation descriptions. Additionally, since data transmission is by packetized Internet Protocol (IP), data can be received and commands initialed from practically any location with no infrastructure. The WFF, like most ranges, has been using GPS receivers on sounding rockets and long duration balloons for several years, The WFF Flight Modem contains a GPS receiver to provide vehicle position for tracking and vehicle recovery. The system architecture which integrates antennas, GPS receiver, commercial satellite packet data modem. and a single board computer with custom software is described and a number of technical challenges are discussed along with the plan for their resolution. These include antenna development, high Doppler rates, reliability, environmental ruggedness, hand over between satellites and data security. An aggressive test plan is included which in addition to environmental Testing measures bit error rate latency and antenna patterns. Additional flight tests are planned far the near future on aircraft, long duration balloons and sounding rockets and these results as well as the current status of the project arc reported. Use of the WFF Flight Modem on small satellites is also being pursued. The LEO satellite constellation altitude above 1400 km is not an obstacle because most spacecraft do not require continuous Communications. The challenge is scheduling where store and forward techniques for command are required and downlink when the communications link allows connection (above 60 percent of the time depending on the satellite altitude). Sophisticated scheduling techniques utilizing 2-line orbital element sets available on the NASA/NORAD Internet site could be implemented for rare special cases. The current 9600 baud rate of the LEO communications link may be increased With special techniques that are planned for development in the WFF Flight Modem project.

Morgan, Dwayne↗
NASA NTRS2007

Design Application Translates 2-D Graphics to 3-D Surfaces

Fabric Images Inc., specializing in the printing and manufacturing of fabric tension architecture for the retail, museum, and exhibit/tradeshow communities, designed software to translate 2-D graphics for 3-D surfaces prior to print production. Fabric Images' fabric-flattening design process models a 3-D surface based on computer-aided design (CAD) specifications. The surface geometry of the model is used to form a 2-D template, similar to a flattening process developed by NASA's Glenn Research Center. This template or pattern is then applied in the development of a 2-D graphic layout. Benefits of this process include 11.5 percent time savings per project, less material wasted, and the ability to improve upon graphic techniques and offer new design services. Partners include Exhibitgroup/Giltspur (end-user client: TAC Air, a division of Truman Arnold Companies Inc.), Jack Morton Worldwide (end-user client: Nickelodeon), as well as 3D Exhibits Inc., and MG Design Associates Corp.

Source record↗
DOE OSTI2025

Allosteric prediction via convolutional neural networks and protein structural and dynamical features

Allostery is the phenomenon whereby a binding event or covalent modification at one site in a protein modulates function at a distal site, thus changing a protein’s functional state. As such, it is a ubiquitous aspect of protein functional regulation. Computationally predicting allosteric states is important as part of the broader challenge of functional annotation, but it also has practical implications for drug development, as targeting an allosteric site often affords greater specificity compared with targeting an orthosteric site. This study introduces a machine learning approach to predict the allosteric functional state using the small G-protein KRas as the model system, due to its implication in many types of cancer and being well studied as a result with many x-ray crystallographic structures of KRas available with different mutations and ligands bound. Using structural and dynamical features that can be cast as images, namely interatomic distances, contact maps, covariance, and mutual information, supervised learning was performed using convolutional neural networks. Two pretrained convolutional neural network architectures, GoogLeNet and ResNet18, were fine-tuned to classify KRas into active or inactive states based on these features. Across training regimes, atomic contact maps emerged as the most effective structural feature, whereas linearized mutual information outperformed covariance in capturing dynamical correlations relevant to allostery. Models achieved significant validation accuracy, with atomic contact maps yielding up to 90% accuracy. In conclusion, the findings suggest that integrating global structural rearrangements and correlated motion patterns with deep learning can reliably predict protein allosteric states, offering a promising framework for understanding allosteric regulation and developing targeted therapeutics.

Rajeshwar T., Rajitha [Oak Ridge National Laborato↗
DOE OSTI2024

Predicting seismic amplitudes with machine learning

The accurate estimation of seismic wave amplitude is vital to precisely determine the yield, magnitude, and event discrimination possible for a given network – a critical element in nuclear explosion monitoring. This task is complicated by several factors, including but not limited to radiation pattern, scattering effects, and crustal variations, which can lead to the attenuation or amplification of amplitude along a given raypath. In this report, we explore the novel application of machine learning to the task of seismic amplitude estimation by training a simple Artificial Neural Network (ANN) on an S-wave amplitude dataset from Lai et al. (2019). Attributes from this dataset used as input to the ANN included event-station distances, station locations (latitude, longitude), event locations (latitude, longitude), event depths, event magnitudes, radiation patterns, signal-to noise ratio (SNR) measurements (average-amplitude, peak-to-trough, maximum peak), and signal periods. We find that the trained ANN predicts S-wave amplitudes with a modest tendency toward underestimating the actual values, as indicated by a linear regression between predicted and actual data (slope: 0.892, intercept: -0.651). These results suggest that an ANN can perform this task, with potential for significant improvements through improved datasets, architectures, and parameter tuning.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS2003

Block QCA Fault-Tolerant Logic Gates

Suitably patterned arrays (blocks) of quantum-dot cellular automata (QCA) have been proposed as fault-tolerant universal logic gates. These block QCA gates could be used to realize the potential of QCA for further miniaturization, reduction of power consumption, increase in switching speed, and increased degree of integration of very-large-scale integrated (VLSI) electronic circuits. The limitations of conventional VLSI circuitry, the basic principle of operation of QCA, and the potential advantages of QCA-based VLSI circuitry were described in several NASA Tech Briefs articles, namely Implementing Permutation Matrices by Use of Quantum Dots (NPO-20801), Vol. 25, No. 10 (October 2001), page 42; Compact Interconnection Networks Based on Quantum Dots (NPO-20855) Vol. 27, No. 1 (January 2003), page 32; Bit-Serial Adder Based on Quantum Dots (NPO-20869), Vol. 27, No. 1 (January 2003), page 35; and Hybrid VLSI/QCA Architecture for Computing FFTs (NPO-20923), which follows this article. To recapitulate the principle of operation (greatly oversimplified because of the limitation on space available for this article): A quantum-dot cellular automata contains four quantum dots positioned at or between the corners of a square cell. The cell contains two extra mobile electrons that can tunnel (in the quantummechanical sense) between neighboring dots within the cell. The Coulomb repulsion between the two electrons tends to make them occupy antipodal dots in the cell. For an isolated cell, there are two energetically equivalent arrangements (denoted polarization states) of the extra electrons. The cell polarization is used to encode binary information. Because the polarization of a nonisolated cell depends on Coulomb-repulsion interactions with neighboring cells, universal logic gates and binary wires could be constructed, in principle, by arraying QCA of suitable design in suitable patterns. Heretofore, researchers have recognized two major obstacles to realization of QCA-based logic gates: One is the need for (and the difficulty of attaining) operation of QCA circuitry at room temperature or, for that matter, at any temperature above a few Kelvins. It has been theorized that room-temperature operation could be made possible by constructing QCA as molecular-scale devices. However, in approaching the lower limit of miniaturization at the molecular level, it becomes increasingly imperative to overcome the second major obstacle, which is the need for (and the difficulty of attaining) high precision in the alignments of adjacent QCA in order to ensure the correct interactions among the quantum dots.

Firjany, Amir↗
NASA NTRS2013

Weight Assessment for Fuselage Shielding on Aircraft With Open-Rotor Engines and Composite Blade Loss

The Federal Aviation Administration (FAA) has been engaged in discussions with airframe and engine manufacturers concerning regulations that would apply to new technology fuel efficient "openrotor" engines. Existing regulations for the engines and airframe did not envision features of these engines that include eliminating the fan blade containment systems and including two rows of counter-rotating blades. Damage to the airframe from a failed blade could potentially be catastrophic. Therefore the feasibility of using aircraft fuselage shielding was investigated. In order to establish the feasibility of this shielding, a study was conducted to provide an estimate for the fuselage shielding weight required to provide protection from an open-rotor blade loss. This estimate was generated using a two-step procedure. First, a trajectory analysis was performed to determine the blade orientation and velocity at the point of impact with the fuselage. The trajectory analysis also showed that a blade dispersion angle of 3deg bounded the probable dispersion pattern and so was used for the weight estimate. Next, a finite element impact analysis was performed to determine the required shielding thickness to prevent fuselage penetration. The impact analysis was conducted using an FAA-provided composite blade geometry. The fuselage geometry was based on a medium-sized passenger composite airframe. In the analysis, both the blade and fuselage were assumed to be constructed from a T700S/PR520 triaxially-braided composite architecture. Sufficient test data on T700S/PR520 is available to enable reliable analysis, and also demonstrate its good impact resistance properties. This system was also used in modeling the surrogate blade. The estimated additional weight required for fuselage shielding for a wing- mounted counterrotating open-rotor blade is 236 lb per aircraft. This estimate is based on the shielding material serving the dual use of shielding and fuselage structure. If the shielding material is not used for dual purpose, and is only used for shielding, then the additional weight per aircraft is estimated to be 428 lb. This weight estimate is based upon a number of assumptions that would need to be revised when applying this concept to an actual airplane design. For example, the weight savings that will result when there is no fan blade containment system, manufacturing limitations which may increase the weight where variable thicknesses was assumed, engine placement on the wing versus aft fuselage, etc.

aircraft safety↗
DOE OSTI2021

Controlled Fabrication of Quality ZnO NWs/CNTs and ZnO NWs/Gr Heterostructures via Direct Two-Step CVD Method

A novel and advanced approach of growing zinc oxide nanowires (ZnO NWs) directly on single-walled carbon nanotubes (SWCNTs) and graphene (Gr) surfaces has been demonstrated through the successful formation of 1D–1D and 1D–2D heterostructure interfaces. The direct two-step chemical vapor deposition (CVD) method was utilized to ensure high-quality materials’ synthesis and scalable production of different architectures. Iron-based universal compound molecular ink was used as a catalyst in both processes (a) to form a monolayer of horizontally defined networks of SWCNTs interfaced with vertically oriented ZnO NWs and (b) to grow densely packed ZnO NWs directly on a graphene surface. We show here that our universal compound molecular ink is efficient and selective in the direct synthesis of ZnO NWs/CNTs and ZnO NWs/Gr heterostructures. Heterostructures were also selectively patterned through different fabrication techniques and grown in predefined locations, demonstrating an ability to control materials’ placement and morphology. Several characterization tools were employed to interrogate the prepared heterostructures. ZnO NWs were shown to grow uniformly over the network of SWCNTs, and much denser packed vertically oriented ZnO NWs were produced on graphene thin films. Such heterostructures can be used widely in many potential applications, such as photocatalysts, supercapacitors, solar cells, piezoelectric or thermal actuators, as well as chemical or biological sensors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1989

A Cerebellar-model Associative Memory as a Generalized Random-access Memory

A versatile neural-net model is explained in terms familiar to computer scientists and engineers. It is called the sparse distributed memory, and it is a random-access memory for very long words (for patterns with thousands of bits). Its potential utility is the result of several factors: (1) a large pattern representing an object or a scene or a moment can encode a large amount of information about what it represents; (2) this information can serve as an address to the memory, and it can also serve as data; (3) the memory is noise tolerant--the information need not be exact; (4) the memory can be made arbitrarily large and hence an arbitrary amount of information can be stored in it; and (5) the architecture is inherently parallel, allowing large memories to be fast. Such memories can become important components of future computers.

Kanerva, Pentti↗
NASA NTRS1995

Multiphase complete exchange on Paragon, SP2 and CS-2

The overhead of interprocessor communication is a major factor in limiting the performance of parallel computer systems. The complete exchange is the severest communication pattern in that it requires each processor to send a distinct message to every other processor. This pattern is at the heart of many important parallel applications. On hypercubes, multiphase complete exchange has been developed and shown to provide optimal performance over varying message sizes. Most commercial multicomputer systems do not have a hypercube interconnect. However, they use special purpose hardware and dedicated communication processors to achieve very high performance communication and can be made to emulate the hypercube quite well. Multiphase complete exchange has been implemented on three contemporary parallel architectures: the Intel Paragon, IBM SP2 and Meiko CS-2. The essential features of these machines are described and their basic interprocessor communication overheads are discussed. The performance of multiphase complete exchange is evaluated on each machine. It is shown that the theoretical ideas developed for hypercubes are also applicable in practice to these machines and that multiphase complete exchange can lead to major savings in execution time over traditional solutions.

Bokhari, Shahid H.↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2020

Nonlinear fluorescence spectroscopy of layered perovskite quantum wells

Interest in layered organohalide perovskites is motivated by their potential for use in optoelectronic devices. In these systems, the smallest and largest quantum wells are primarily concentrated near the glass and air interfaces of a film, thereby establishing a gradient in the average values of the bandgaps. It has been suggested that this layered architecture promotes the funneling of electronic excitations through space in a manner similar to light-harvesting processes in photosynthetic antennae. Whereas energy and charge transfer are difficult to distinguish by conventional transient absorption techniques, it has recently been shown that these competing relaxation mechanisms may be separately targeted with nonlinear fluorescence (NLFL) and photocurrent “action spectroscopies.” Here, we present perturbative rate functions to describe NLFL experiments conducted on layered perovskite systems. The formulas reproduce the patterns of resonances observed in experimental measurements and show how signatures of energy transfer manifest in two-dimensional spectra. Overall, this work suggests that NLFL spectroscopy may be used to fully reveal the trajectories of electronic excitations by correlating ultrafast energy transfer pathways to fluorescence emission from the thickest quantum wells.

Ouyang, Zhenyu↗
NASA NTRS2016

Computational Modeling to Limit the Impact Displays and Indicator Lights Have on Habitable Volume Operational Lighting Constraints

Even with no ambient lighting system "on", the International Space Station glows at night. The glow is caused by indicator lamps and displays that are not included with the specification of the ambient lighting system. How does this impact efforts to improve the astronaut's lighting environment to promote more effective sleep patterns? Do the extra indicators and displays add enough light to change the spectrum of light the crew sees during the day as well? If spacecraft environments are specifically engineered to have an ambient lighting system that emits a spectrum promoting a healthy circadian response, is there a way control the impact? The goal of this project is to investigate how additional light sources, such as displays and indicators change the effective light spectrum of the architectural lighting system and how impacts can be mitigated.

Clark, T. A.↗
DOE OSTI2026

Compressive Response and Energy Absorption of Additively Manufactured Elastomers with Varied Simple Cubic Architectures

Additive manufacturing, and particularly the vat photopolymerization process, enables the fabrication of complex geometries at high resolution and small length scales, making it well-suited for fabricating cellular structures (e.g., foams and lattices). Among these, elastomeric cellular structures are of growing interest due to their tunable compliance and energy dissipation. However, comprehensive data on the compressive behavior of these structures remains limited, especially for investigating the structure-property effects from changing the density and distribution of material within the cellular structure. This study explores how the mechanical response of polyurethane-based simple cubic structures changes when varying volume fraction, unit cell length, and unit cell patterning, which have not been systematically investigated previously in additively manufactured elastomers. Increasing volume fraction from 10% to 50% yielded significant changes in compressive stress–strain performance (decreasing strain at 0.5 MPa by 41.6% and increasing energy absorption density by 3962.5%). Although changing the unit cell length between 2.5 and 7 mm in ~30 mm parts did not result in statistically different stress–strain responses, modifying the configuration of struts of different thicknesses across designs with 30% volume fraction altered the stress–strain behavior (differences of 12.5% in strain at 0.5 MPa and 109.4% for energy absorption density). Power law relationships were developed to understand the interactions between volume fraction, unit cell length, and elastic modulus, and experimental data showed strong fits (R 2 > 0.91). These findings enhance the understanding of how multiple structural design aspects influence the performance of elastomeric cellular materials, providing a foundation for informing strategic design of tailorable materials for diverse mechanical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Amphiphile conformation impacts aggregate morphology and solution structure across multiple lengthscales

Although the self-assembly of amphiphiles is well-studied in aqueous solutions, much less is understood about the fundamental driving forces and structure property relationships in non-polar media. In recent work [Journal of Physical Chemistry B, 2020, 124, 10822.] the authors have studied a series of malonomide-based amphiphiles that are relevant to liquid-liquid extraction. That work demonstrated that aggregation is largely driven by local dipole-dipole interactions between molecules. Here, we build upon this observation to develop a more detailed understanding of how the balance of dipole-dipole interactions (controlled by conformation) and molecular architecture influences the morphology of the aggregates across lengthscales. Using constrained molecular dynamics about key degrees of freedom, we demonstrate that the conformation of N,N-dimethyl,N,N-dioctylhexylethoxy malonamide (DMDOHEMA) and N,N-dimethyl,N,N-dibutyltetradecyl malonamide (DMDBTDMA) has a significant impact upon self-association - where appropriate conformational sampling is essential. To quantify the aggregate morphology, several graph theoretic and persistent homology based properties are determined. The former examines the patterns of intermolecular interactions within clusters, while the latter examines the 3-dimensional spatial distribution. Based upon these analyses, we find that the morphology of aggregates, particularly at higher malonamide concentration, depends on a balance of dipole alignment and alkyl tail sterics. Dipole alignment encourages linear patterns of the intermolecular interactions within aggregates, while the alkyl tail steric interactions between the malonamide result in noticeably less linear aggregates for DMDOHEMA than DMDBTDMA. This is reflected in the spatial distribution, where more holes or voids exist between extractants within the DMDOHEMA that distribute within the solution in more of a "swiss cheese" arrangement as opposed to the more filamentous distribution of DMBDTDMA. This study links conformation and molecular structure to the morphology of amphiphile assemblies, and serves as a basis for ongoing study of multicomponent amphiphile solutions with polar and other solutes, and how these impact aggregation phenomena.

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

Illuminated nights accelerate the migration‐linked phenology in a passerine finch redheaded bunting

Abstract The excessive use of artificial light is altering the natural light–dark cycles, consequently impacting animal behavior and physiology. Dim light at night (dLAN) can disrupt migratory patterns, alter hormone levels, and impact breeding success in birds. The present research aims to address the effects of dLAN on the metabolic and reproductive tissues of migratory redheaded bunting ( Emberiza bruniceps ). For this, buntings under short winter‐like days (10L:14D) were exposed to either dark nights ( D = 0.00014 W/m 2 ) or dLAN ( D = 0.058 W/m 2 ), and their locomotor activity, body mass, fat score, food intake, testicular volume, and plasma testosterone levels were measured. The histological architecture of the muscle, intestine, testis, and liver tissues was assessed. Birds exposed to dark nights confined their activity to the daytime only, whereas the dLAN group showed nocturnal activity and initiated Zugunruhe (nighttime restlessness). The body mass, food intake, fat score, and testicular volume significantly increased under dLAN. Histomorphometry revealed increased muscle width, epithelium thickness, and lumen diameter in the testis, proximal and distal muscularis thickness in the intestine, hepatic lipid droplet size, and decreased proximal villi length and intestinal diameter under dLAN. Further, plasma testosterone levels also increased under dLAN. Our results suggest that dLAN can induce migration‐linked phenotypes even under non‐stimulatory short days leading to mistimed seasonal activities.

Tiwari, Jyoti [Department of Zoology University of↗
DOE OSTI2025

$\mathrm{SageNet}$: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation

Accurate modeling of the inflationary gravitational waves (GWs) requires time-consuming, iterative numerical integrations of differential equations to take into account their backreaction on the expansion history. To improve computational efficiency while preserving accuracy, we present the Stiff-amplified Gravitational-wave Emulator Network (SageNet), a deep learning framework designed to replace conventional numerical solvers (code available at https://github.com/YifangLuo/SageNet). SageNet employs a long short-term memory architecture to emulate the present-day energy density spectrum of the inflationary GWs with possible stiff amplification, Ω GW (f). Trained on a data set of 25,689 numerically generated solutions, SageNet allows accurate reconstructions of Ω GW (f) and generalizes well to a wide range of cosmological parameters; 90.9% of the test emulations with randomly distributed parameters exhibit errors of under 4%. In addition, SageNet demonstrates its ability to learn and reproduce the artificial, adaptive sampling patterns in numerical calculations, which implement denser sampling of frequencies around changes in spectral indices in Ω GW (f). The dual capability of learning both physical and artificial features of the numerical GW spectra establishes SageNet as a robust alternative to exact numerical methods. Finally, our benchmark tests show that SageNet reduces the computation time from tens of seconds to milliseconds, achieving a speedup of ∼10 4 times over standard CPU-based numerical solvers with the potential for further acceleration on GPU hardware. These capabilities make SageNet a powerful tool for accelerating Bayesian inference procedures for extended cosmological models. In a broad sense, the SageNet framework offers a fast, accurate, and generalizable solution to modeling cosmological observables whose theoretical predictions demand costly differential equation solvers.

Astronomy data modeling↗