Universities Space Research Association

In this study, Al/Zr multilayer thin films were synthesized using pulsed laser deposition (PLD) with a 248 nm KrF excimer laser to investigate their structural and surface properties for extreme ultraviolet (EUV) optical and protective coatings. Alternating Al and Zr layers were deposited on Si substrates under high vacuum, with nanometer-scale thickness control achieved via spectroscopic ellipsometry. X-ray diffraction revealed enhanced crystallinity at 400 °C, notably with strong Zr(002) orientation. SEM and AFM showed a morphological transition from smooth 2D to rough 3D growth at 500 °C, attributed to increased adatom mobility and interfacial strain. SEM images of Al at 400 °C exhibited a uniform nanocrystalline surface with compact grains and low porosity, suggesting stable 2D growth. Computational modeling based on free energy minimization supported the 2D–3D transition, predicting a critical film thickness of 1–2 nm, consistent with observations. These results demonstrate PLD’s capability to produce high-quality Al/Zr multilayers for EUV mirrors and advanced coatings.

Nominally anhydrous minerals (NAMS), such as plagioclase, may represent a significant volatile reservoir as their low intrinsic volatile content is multiplied many times by their large volumetric abundance. This study reports the volatile concentration of CO 2 , H 2 O, F, P 2 O 5 , S, and Cl in shocked plagioclase from three paired lunar troctolites, Northwest Africa (NWA) 5744, NWA 8676, and NWA 10140, to assess the volatile inventory of the early lunar crust. Measured volatile abundances are typically < 5 ppm, except for P 2 O 5 , which is as high as ∼ 300 ppm. Calculated parental melt estimates range from ∼ 3.2 to 238 ppm H 2 O, 2.3 to 213 ppm F, and 0.13 to 1971 ppm Cl. Whilst the lower ends of these ranges are in keeping with literature values, the higher ends of these ranges may indicate heterogeneity in the early Moon's volatile distribution. The cause or causes of any heterogeneity will remain poorly defined until complementary isotopic data (e.g., H, Cl) and distribution coefficients are known for lunar conditions.

The integration of artificial intelligence (AI) and affective computing into behavioral health is transforming how mental wellbeing is assessed, monitored, and treated. As emotional and cognitive states can now be inferred through facial expressions, vocal tone, physiological signals, and behavioral cues, new technological paradigms are emerging to complement traditional approaches to mental healthcare. This is especially relevant in the wake of rising global mental health challenges, where access, personalization, and real-time feedback are essential components of effective care. Affective computing, a multidisciplinary field at the intersection of psychology, computer science, and cognitive science, seeks to enable machines to recognize, interpret, and respond to human emotions. When coupled with AI-driven data analytics and virtual reality (VR), it offers powerful tools for enhancing self-awareness, supporting clinical diagnostics, and delivering immersive therapeutic interventions. This paper explores how AI and affective computing can be leveraged across the behavioral health spectrum, from early detection and remote monitoring to therapy delivery and outcome prediction, with a particular emphasis on virtual environments as mediators of emotionally adaptive systems. We aim to review current innovations, examine their psychological validity, discuss ethical implications, and propose a research framework for advancing human-centered AI in behavioral health. Through this lens, we highlight the potential of emotionally intelligent systems not only to augment clinical practice but also to empower users in managing their mental wellbeing in real time.

Lightweight cryogenic propellant storage solutions with zero or near zero boil-off represent a critical need for NASA in both aeronautics and space technology missions. Applications for composite cryogen storage tanks range from hydrogen powered aircraft to long-duration in-space and on-surface missions. Over the past several decades, NASA and industry partners have supported several notable composite cryotank development programs documenting lessons learned with respect to materials and manufacturing. This paper specifically reports the results of material screening efforts funded by both NASA’s Aeronautics Research Mission Directorate and Space Technology Mission Directorate to evaluate thermoset and thermoplastic composites for cryogen storage. Although the end applications differ, the fundamental material requirements across the two projects are the same. This paper details characterization of candidate thermoplastic and thermoset composite materials for cryogen storage, with test data generated (1) at -196oC and (2) following 100 thermal cycles between room temperature (21oC) and LN2 temperature (-196oC).

This paper presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2 million global time series samples from NASA’s Harmonized Landsat and Sentinel-2 data archive at 30-m resolution, the new model incorporates temporal and location embeddings for enhanced performance across various geospatial tasks. Through extensive benchmarking with GEO-Bench, the model outperforms the previous Prithvi-EO model by 8% across a range of tasks. It also outperforms six other geospatial foundation models when benchmarked on remote sensing tasks from different domains and resolutions (i.e. from 0.1 m to 15 m). The results demonstrate the versatility of the model in both classical Earth observation and high-resolution applications. Early involvement of end-users and subject matter experts (SMEs) allowed constant feedback on model and dataset design, enabling customization across diverse SME-led applications in disaster response, land cover and crop mapping, and ecosystem dynamics monitoring. Prithvi-EO-2.0 is available as an open-source model on Hugging Face and IBM TerraTorch, with additional resources on GitHub. The project exemplifies the Trusted Open Science approach embraced by all involved organizations.

Abstract Stars form in dense, cold regions of interstellar clouds where gravity, turbulence, and magnetic fields interact. Magnetic fields are expected to regulate how gas contracts, but their role at the earliest, prestellar stage is poorly constrained, because polarized dust emission from the coldest cores is faint. Here we report submillimeter dust polarization observations of the well-studied prestellar core L1544, obtained with the James Clerk Maxwell Telescope SCUBA-2/POL-2 instrument as part of the BISTRO (B-fields in Star-forming Region Observations) survey. The data resolve a clearly pinched, hourglass-shaped magnetic field at the core center, representing the first detection in a prestellar core. The plane-of-sky field morphology matches classical predictions for magnetically regulated contraction and is consistent with previously reported slow infall and filament-fed accretion flows that deliver material into the core. From the polarization angle dispersion, we infer a magnetic field strength that implies an Alfvén speed above the measured infall speed and well below the free-fall velocity, indicating sub-Alfvénic, magnetically moderated contraction. These results show that the hourglass-shaped field geometry reported in some protostellar envelopes can already be present before a protostar forms, thereby bridging a key observational gap in the timeline of magnetically regulated star formation. As a nearby, representative core on the verge of star formation, L1544 now serves as a benchmark for testing theoretical models that couple gravity and magnetic fields in dense cores.

Abstract We present the X-ray polarization observation of G21.5−0.9, a young Galactic supernova remnant (SNR), conducted with the Imaging X-ray Polarimetry Explorer (IXPE) in 2023 October, with a total livetime of approximately 837 ks. Using different analysis methods, such as a space-integrated study of the entire region of the pulsar wind nebula (PWN) and a space-resolved polarization map, we detect significant polarization from the PWN at the center of the SNR, with an average polarization degree of ∼10% oriented at ∼33° (north through east). No significant energy-dependent variation in polarization is observed across the IXPE band (2–8 keV). The polarization map, corrected for the effect of polarization leakage, reveals a consistent pattern in both degree and angle, with little change across the nebula. Our findings indicate the presence of a highly polarized central torus, suggesting low levels of turbulence at particle acceleration sites. Unlike Vela, but similar to the Crab Nebula, we observe substantial differences between radio and X-ray polarization maps. This suggests a clear separation in energy of the emitting particle populations and hints at an important, yet poorly understood, role of instabilities in the turbulence dynamics of PWNe.

Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of quantum advantage and high resource estimates, especially due to the need for optimization in the presence of noise. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for variational quantum eigensolver or more broad applications of hybrid methods in which optimization is required. To this goal, we look to use modern approaches developed in circuit simulations and stochastic classical optimization, which can be combined to form a surrogate optimization approach to quantum circuits. Using an approximate (classical central processing unit/graphical processing unit) state vector simulator as a surrogate model, we efficiently calculate an approximate Hessian, which is passed as input for a quantum processing unit or exact circuit simulator. This method will lend itself well to parallelization across quantum processing units. We demonstrate the capabilities of such an approach with and without sampling noise and a proof-of-principle demonstration on a quantum processing unit utilizing 40 qubits.