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A study was conducted to identify problems related to sampling the Earth's radiant energy budget and to define a satellite system with sufficient sampling to satisfy science requirements on global, zonal, and regional scales.
Reference is made to the measurements of a cosmic ray antiproton flux at a few hundred MeV reported by Buffington et al. (1981), noting that one of the final background processes to be removed by the data analysis in that study was helium-induced events which satisfied the criteria for topology and timing. The response in the third scintillator S3 was used to identify and remove these events. For the top two scintillators S1 and S2, pulse size information was lost during the data-taking. A method is reported here for the partial retrieval of pulse size information for the scintillator S2. This is possible because a portion of this signal was subtracted from the Cerenkov response before trigger discrimination and data recording to remove scintillation from the Cerenkov response. For separating protons from more highly charged particles, the method is considered sufficient. It is pointed out that the sample of events identified as antiprotons, for which the method can be applied, has the expected unit charge in scintillator S2.
With a revived focus to create a sustainable hu-man presence on the moon, and in preparation for future Mars exploration, it is imperative that all resources are utilized to their fullest potential [1]. In-Situ Resource Utilization (ISRU) will be critical for future mission success as it would enable in-dependent operation of missions while reducing the dependence on the complex supply chain created between the Earth, the moon and Mars. One of the most critical resources that has been identified for ISRU on the lunar surface is water [2]. Water is a versatile resource that can be used in various space operations, such as ra-diation shielding and conversion to oxygen (O2) for propellant [3,4]. One of the most water rich resources is thought to be within the regolith lo-cated in the permanently shadowed regions (PSR) of the Moon; several upcoming missions, such as PRIME-1 and VIPER, are already sched-uled to confirm this hypothesis [5,6]. However, there are currently no missions planned to quanti-fy the abudance of water at these regions. The quantity of water contained in the ice will inform and help the development of future ISRU plants and operation. Water and other volatile gases can be detect-ed upon controlled heating of icy lunar regolith for an accurate quantification of water. This method is similar to thermogravimetric analysis, which was used to analyze Apollo lunar regolith sam-ples. However, losses via sublimation/evaporation or contamination may have occurred with the samples due to the transit and exposure to the terrestrial environment. Furthermore, water would not be expected for these samples since they were not collected from a PSR. Thus, for more accurate results, icy lunar regolith should be ana-lyzed in situ to provide the most representative composition of both water and other volatile gas-es. The Light Water Analysis and Volatile Extract (LightWAVE) project aims to address this knowledge gap. A system has been developed to collect, heat and analyze evolved gases from lu-nar regolith on the lunar surface. The gases will be analyzed by a modified commercial off the shelf (COTS) residual gas analyzer (RGA) quad-rupole mass spectrometer. Evolved gases from heated regolith samples can be identified by the observed mass-to-charge (m/z) ratios, and the peak intensities correlate to the abundances of the observed gases for quantitation. Therefore, our group is developing a calibration methodology utilizing a COTS RGA to quantitate water from in-situ regolith samples on the lunar surface. The partial pressure of water and ideal gas law will be used to quantify the total water evolved from a regolith sample. Here we present our methodol-ogy for the design and calibration for water quan-titation using a COTS RGA.
With its historical mission as the focal point for production of uranium components for the NNSA nuclear weapons program, the National Nuclear Materials Archive (NNMA) makes use of Y-12's wide range of uranium materials processing knowledge and onsite materials to identify, collect, and preserve sample materials for nuclear forensics purposes. In 2019, Y-12 took on the responsibility for identifying, subsampling, and shipping a total of 24 NNMA samples to the Lawrence Livermore National Laboratory (LLNL) for further forensics analysis. These were highly enriched uranium metal pieces representative of different weapon systems components produced by Y-12 from approximately 1963 to 1993. The purpose was to provide the NNMA program with higher precision analyses of these materials than what is currently available from historical Y-12 production stream data.
Abstract Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance Statement A typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.
This data set contains metadata associated with peat core samples collected from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in August 2025. This sample metadata contains no analytical results and is a reference for analytical datasets. To ensure accessibility and discoverability, each sample was assigned an International Generic Sample Number (IGSN), a persistent identifier, using System for Earth and Extraterrestrial Sample Registration (SESAR). These samples were used for downstream analysis by multiple teams of researchers the results of which will be reported separately. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format. An aliquot of most samples is stored in the SPRUCE archive and may be available for further analysis by request. Access this collection event on SESAR https://doi.org/10.58052/IEJ9B05LW. To inquire about obtaining archived samples for analysis, reach out using the Contact Sample Owner form located on the bottom of the landing page in SESAR.
This data set contains metadata associated with shrub-layer vegetation samples collected from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in August 2025. This sample metadata contains no analytical results and is a reference for analytical datasets. To ensure accessibility and discoverability, each sample was assigned an International Generic Sample Number (IGSN), a persistent identifier, using System for Earth and Extraterrestrial Sample Registration (SESAR). These samples were used for downstream analysis by multiple teams of researchers the results of which will be reported separately. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format. An aliquot of most samples is stored in the SPRUCE archive and may be available for further analysis by request. See below under 7 Sample Access. Access this collection event on SESAR https://doi.org/10.58052/IEJ9B069L. To inquire about obtaining archived samples for analysis, reach out using the Contact Sample Owner form located on the bottom of the landing page in SESAR.
The 13 samples from Mars identified in the terrestrial meteorite collections vary from dunite to pyroxenite to microgabbro or basalt. All of these rocks appear to have formed from primitive melts with similar major element compositional characteristics; i.e., FeO-rich and Al2O3-Poor melts relative to terrestrial basalt compositions. Although all of the SNC rocks can be derived by melting of the same Al-depleted mantle, contamination of SNC's by a Rb-enriched mantle or crustal source is required to explain the different REE characteristics of SNC rocks. Thus, there are indications of an old crustal rocktype on Mars, and this rock does not appear to have been sampled. This paper focuses primarily on the composition of the SNC basalts, however, and on the compositions of rocks which could be derived from SNC basaltic melt by magmatic processes. In particular, we consider the possible compositions which could be achieved through accumulation of early-formed crystals in the SNC primitive magma. Through a set of experiments we have determined (1) melt (magma) compositions which could be produced by melt evolution as crystals are removed from batches of this magma cooling at depth, and (2) which evolved (Si02enriched, MgO-depleted) rock compositions could be produced from the SNC magma, and how these compare with the Pathfinder andesite composition. Finally, we compare the SNC magma compositions to the Mars soil composition in order to determine whether any source other than SNC is required.
Representative in vitro model systems that accurately model response to therapy and allow the identification of new targets are important for improving our treatment of prostate cancer. Here we describe molecular characterization and drug testing in a panel of 20 prostate cancer cell lines. The cell lines cluster into distinct subsets based on RNA expression, which is largely driven by functional Androgen Receptor (AR) expression. KLK3 , the AR-responsive gene that encodes prostate specific antigen, shows the greatest variability in expression across the cell line panel. Other common prostate cancer associated genes such as TMPRSS2 and ERG show similar expression patterns. Copy number analysis demonstrates that many of the most commonly gained (including regions containing TERC and MYC ) and lost regions (including regions containing TP53 and PTEN) that were identified in patient samples by the TCGA are mirrored in the prostate cancer cell lines. Assessment of response to the anti-androgen enzalutamide shows a distinct separation of responders and non-responders, predominantly related to status of wild-type AR. Surprisingly, several AR-null lines responded to enzalutamide. These AR-null, enzalutamide-responsive cells were characterized by high levels of expression of glucocorticoid receptor (GR) encoded by NR3C1 . Treatment of these cells with the anti-GR agent mifepristone showed that they were more sensitive to this drug than enzalutamide, as were several of the enzalutamide non-responsive lines. This is consistent with several recent reports that suggest that GR expression is an alternative signaling mechanism that can bypass AR blockade. This study reinforces the utility of large cell line panels for the study of cancer and identifies several cell lines that represent ideal models to study AR-null cells that have upregulated GR to sustain growth.
We present a survey of variable stars detected in K2 Campaign 13 within the massive intermediate-age (∼1 Gyr) open cluster NGC 1817. We identify a complete sample of 44 red clump stars in the cluster, and have measured asteroseismic quantities (ν {sub max} and/or Δν) for 29 of them. Five stars showed suppressed dipole modes, and the occurrence rates indicate that mode suppression is unaffected by evolution through core helium burning. A subset of the giants in NGC 1817 (and in the similarly aged cluster NGC 6811) have ν {sub max} and Δν values at or near the maximum observed for core helium–burning stars, indicating they have core masses near the minimum for fully nondegenerate helium ignition. Further asteroseismic study of these stars can constrain the minimum helium core mass in red clump stars and the physics that determines this limit. Two giant stars show photometric variations on timescales similar to previously measured spectroscopic orbits. Thirteen systems in the field show eclipses, but only five are probable cluster members. We identify 32 δ Sct pulsators, 27 γ Dor candidates, and 7 hybrids that are probable cluster members, with most being new detections. We used the ensemble properties of the δ Sct stars to identify stars with possible radial pulsation modes. Among the oddities we have uncovered are: an eccentric orbit for a short-period binary containing a δ Sct pulsating star; a rare subgiant within the Hertzsprung gap showing δ Sct pulsations; and two hot γ Dor pulsating star candidates.
We analyze the properties of a sample of galaxies identified in a 21cm, H I-line survey of selected areas in the Perseus-Pisces supercluster and its foreground void. Twelve fields were observed in the supercluster, five of them (target fields) centered on optically bright galaxies, and the other seven (blank fields) selected to contain no bright galaxies within 45 min. of their centers. We detected nine previously uncatalogued, gas-rich galaxies, six of them in the target fields. We also detected H I from seven previously catalogued galaxies in these fields. Observations in the void covered the same volume as the 12 supercluster fields at the same H I-mass sensitivity, but no objects were detected. Combining out H I data with optical broadband and H alpha imaging, we conclude that the properties of H I-selected galaxies do not differ substantially from those of late-type galaxies found in optical surveys. In particular, the galaxies in our sample do not appear to be unusually faint for their H I mass, or for their circular velocity. We find tentative evidence for a connection between optical surface brightness and degree of isolation, in the sense that low surface brightness galaxies tend to be more isolated. The previously catalogued, optically bright galaxies in our survey volume dominate the total H I mass density and cross section; the uncatalogued galaxies contribute only approximately 19 percent of the mass and approximately 12 percent of the cross section. Thus, existing estimates of the density and cross section of neutral hydrogen, most of which are based on optically selected galaxy samples, are probably accurate. Such estimates can be used to compare the nearby universe to the high-redshift universe probed by quasar absorption lines.
The overall goal of the task was to identify cost effective biological sampling techniques for MHK environmental monitoring. Protected, demersal, and pelagic fish and selected nektonic invertebrates were surveyed using capture and remote sensing techniques at the PacWave sites. The performance of capture and remote sensing monitoring techniques were evaluated and generic nekton monitoring indices were developed for MHK technologies and sites. In parallel to data collections at the PacWave site, the ability of regression models to characterize, detect, and predict change in acoustic data was evaluated using acoustic data collected in Admiralty Inlet, WA, during a biological monitoring study of the proposed SnoPud tidal turbine project site. Expected outcomes of these efforts included an evaluation of instrumentation and techniques used to monitor biological variability; identification of data streams that can be used to detect and quantify change; and sampling requirements to ensure detection of change in monitored variables.
Abstract Currently, many utility‐scale wind turbine generator original equipment manufacturers are dependent on imported rare earth permanent magnets, which are susceptible to market risks from cost instability. To lower the production costs of these generators and stay competitive in the market, several small wind manufacturers are pursuing continuous improvements to both generator design and manufacturing. However, traditional design and manufacturing methods have yielded marginal improvements in wind power performance. This work presents novel methods to redesign a baseline 15‐kW wind turbine generator with reduced rare‐earth permanent magnets by leveraging cutting‐edge three‐dimensional (3D) printed polymer‐bonded permanent magnets and steel. Symmetric, asymmetric, and multimaterial‐magnet parametrization methods are introduced for shape optimization. We extend the symmetric and asymmetric methods to the back iron in the stator to further investigate the impact and opportunities for performance improvements with lesser active materials. We employ a design‐of‐experiments approach with parametric computer‐aided design for shape generation and evaluate different designs by magneto‐thermal modeling and finite‐element analysis. We use adaptive sampling technique to identify better performing designs with lesser magnet mass, higher efficiency, and lower cogging torque when compared with the baseline generator. Asymmetric pole designs resulted in a magnet mass in the range of 4.77–5.37 kg, which was 27%–35% lighter than the baseline generator, suggesting that a new design freedom exists that can be enabled by advanced manufacturing, such as 3D printing. Shaping the back iron in the stator resulted in material savings in electrical steel of up to 14.62 kg, which was 20% lighter than the baseline stator. We conducted a structural analysis to evaluate an optimized asymmetric rotor design from the point of view of mechanical integrity and air‐gap stiffness. The magnetically optimal shape profile was shown as having a positive impact on the radial stiffness, and an optimal solution was discovered to reduce the structural mass by nearly 30 kg, which was 29% lighter than the baseline.
The corrosion behavior of a high creep strength carbide-phase strengthened Ni-based alloy in molten FLiNaK (LiF-NaF-KF: 46.5-11.5-42 mol%) salt in the temperature range of 700°C–750°C has been investigated as a part of the development of structural alloys for fluoride salt-based molten salt reactors (MSRs). The alloy composition was designed based on Hastelloy-N, but with the goal of improving creep strength. Cr depletion depth, a measure of corrosion, was observed to be single micrometers after several hundred hours of corrosion testing. Sequential corrosion testing involving testing of pre-tested samples in fresh salt coupled with SEM-EDS, scanning transmission electron microscopy (STEM) examinations and thermodynamic and kinetic modeling, suggest that the corrosion rate at the alloy-salt interface is governed by diffusion of elements from the alloy bulk to the surface. The carbide phases in the corrosion-tested sample microstructure were identified to be largely M 6 C-type Mo-rich carbides and MC-type mixed carbides. Atom probe tomography (APT) showed some partitioning of Cr and Ti to the carbide phase and showed the carbide phases to be stable at the salt-facing surface.
Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.
Abstract Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.
The addition of nanoparticles (NPs) to polymers is a powerful method to improve the mechanical and other properties of macromolecular materials. Such hybrid polymer–particle systems are also rich in fundamental soft matter physics. Among several factors contributing to mechanical reinforcement, a polymer-mediated NP network is considered to be the most important in polymer nanocomposites (PNCs). Here, we present an integrated experimental–theoretical study of the collective NP dynamics in model PNCs using X-ray photon correlation spectroscopy and microscopic statistical mechanics theory. Silica NPs dispersed in unentangled or entangled poly(2-vinylpyridine) matrices over a range of NP loadings are used. Static collective structure factors of the NP subsystems at temperatures above the bulk glass transition temperature reveal the formation of a network-like microstructure via polymer-mediated bridges at high NP loadings above the percolation threshold. The NP collective relaxation times are up to 3 orders of magnitude longer than the self-diffusion limit of isolated NPs and display a rich dependence with observation wavevector and NP loading. A mode-coupling theory dynamical analysis that incorporates the static polymer-mediated bridging structure and collective motions of NPs is performed. It captures well both the observed scattering wavevector and NP loading dependences of the collective NP dynamics in the unentangled polymer matrix, with modest quantitative deviations emerging for the entangled PNC samples. Here, we identify an unusual and weak temperature dependence of collective NP dynamics, in qualitative contrast with the mechanical response. Hence, the present study has revealed key aspects of the collective motions of NPs connected by polymer bridges in contact with a viscous adsorbing polymer medium and identifies some outstanding remaining challenges for the theoretical understanding of these complex soft materials.
Pump–probe measurements by ultrashort THz pulses can be used to excite and follow the coherence dynamics in the time domain of single hydrogen molecules (H 2 ) in the junction of a scanning tunneling microscope (STM). By tailoring the resonance frequency through the sample bias, we identified two spectral signatures of the interactions among multiple H 2 molecules. First, the avoided level crossing featured by energy gaps ranging from 20 to 80 GHz was observed because of the level repulsion between two H 2 molecules. Second, the tip can sense the signal of H 2 outside the junction through the projective measurement on the H 2 inside the junction, owing to the entangled states created through the interactions. A dipolar-type interaction was integrated into the tunneling two-level system model of H 2 , enabling accurate reproduction of the observed behaviors. Furthermore, our results obtained by the quantum superposition microscope reveal the intricate quantum mechanical interplay among H 2 molecules and additionally provide a 2D platform to investigate unresolved questions of amorphous materials.