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The author has identified the following significant results. Major landforms and some subdivisions could be easily recognized. Water bodies, river courses, extensive areas of miocene clays, and more recent coarse textured deposits could be delineated and existing soil maps at scales up to 1:100,000 could be updated.
The Guinevere Planitia quadrangle of Venus (0-25degN, 300-330deg) covers a lowland region east of Beta Regio and west of Eistla Regio, including parts of Guinevere and Undine Planitiae. The V-30 quadrangle is dominated by low-lying plains interpreted to be of volcanic origin and exhibiting numerous wrinkle ridges. Using Pioneer Venus, Goldstone, and Arecibo data, previous investigators have described radar bright, dark, and mottled plains units in the Guinevere Planitia region, as well as arcuate fracture zones and lineament belt segments that define the Beta-Eistla deformation zone [1-5]. Magellan SAR images show that volcanic landforms compose the majority of the surface units in V-30 [6-7]. The quadrangle contains parts of four major volcanoes: Atanua (9degN, 307deg), Rhpisunt (3degN, 302deg), Tuli (13degN, 314deg), and Var (3degN, 316deg) Montes, and three coronae: Hulda (12degN, 308deg), Madderakka (9degN, 316deg), and Poloznitsa (1degN, 303deg). Seymour crater, located at 18degN, 327deg, is associated with extensive crater outflow deposits.
A combined data analysis and theoretical program aimed at interpreting and utilizing solar wind data obtained from Pioneer 6, Mariner 5, and Explorer 35 has been completed. A theoretical model of the radial dependence of large scale solar wind inhomogeneities was developed and used to map solar wind variations measured by Explorer 35 to various heliocentric distances and to the orbits of Mercury, Venus, and Mars. The model was also used to determine power spectra velocity, density and temperature variations of 20 R sub s from spectra obtained from the Mariner 5 data at an average heliocentric distance of 180 R sub s. Five stream-stream interaction events in the Pioneer 6 data were analyzed which confirmed the picture of a spiral compression ridge interfacing the two streams and the associated east-west deflections of the solar wind flow. Magnetopause crossings observed in Explorer 35 plasma data were used to develop statics on boundary motions at lunar distance. A study of the geomagnetic disturbance field asymmetry was performed and a model of disturbance field from a partial ring current was developed.
The author has identified the following significant results. Analysis of 1972 single-date coverage indicated that a complete crop classification was not attainable at the test sites. Good multi-date coverage during 1973 indicates that many of the problems encountered in 1972 will be minimized. In addition, the compilation of springtime imagery covering the entire state of Iowa has added a new dimension to interpretation of Iowa's natural resources. ERTS-1 has provided data necessary to achieve the broad synoptic view not attainable through other means. This should provide soils and crop researchers and land use planners a base map of Iowa. Granted and due to the resolution of ERTS-1, not all details are observable for many land use planning needs, but this gives a general and current view of Iowa.
The accuracy of mapping and estimating the area of forestlands in southwestern Michigan from winter and fall LANDSAT images was evaluated. All omission and commission errors in the LANDSAT forest maps were identified and mapped through comparison with an existing detailed forest cover type map. Accuracies ranged from 74.0% to 98.5% and were higher for the winter imagery. Most errors (85%) occurred along the perimeter of forestlands and were less than 4 hectares (10 acres) and size. Other factors affecting interpretation are reported as well as time and image availability considerations.
The emplacement history and petrochemical evolution of the volcanics associated with Pine Butte, Crater Butte, and other nearby vents are developed and described. Four major vents were identified in the study area and their associated eruptive products were mapped. All of the vents show a marked physical elongation or linear orientation coincident with the observed rift set. Planetary exploration has revealed the importance of volcanic processes in the genesis and modification of extraterrestrial surfaces. Interpretation of surface features has identified plains-type basaltic volcanism in various mare regions of the Moon and the volcanic provinces of Mars. Identification of these areas with features that appear analogous to those observed in the Pine Butte area suggests similar styles of eruption and mode of emplacement. Such terrestrial analogies serve as a method to interpret the evolution of volcanic planetary surfaces on the inner planets.
Digitally enhanced Landsat Thematic Mapper (TM) images were used to map neotectonic deformation in central Mexico. This region has been studied for decades using a variety of geological and geophysical techniques, but synoptic mapping of neotectonic activity and major fault zones there, and an evaluation of their regional relationship to the character and location of volcanism were not previously possible until the application of synoptic, high resolution satellite imagery. Interpretation of the TM images shows that the tectonic deformation is closely linked in time and space to the dominantly calc-alkaline volcanics of the Mexican Volcanic Belt (MVB). The eruptive style and distribution of the volcanics is clearly related to the deformation resulting from relative motions of three large crustal blocks south of the MVB. Therefore, zones of weakness within the crust of central Mexico, which may be inherited from earlier episodes of deformation, are a principal factor controlling the oblique orientation of the MVB relative to the Acapulco Trench.
The relatively young technology of NanoSIMS is unlocking an exciting new level of information from organic matter in ancient sediments. We are using this technique to characterize Proterozoic organic material that is clearly biogenic as a guide for interpreting controversial organic structures in either terrestrial or extraterrestrial samples. NanoSIMS is secondary ion mass spectrometry for trace element and isotope analysis at sub-micron resolution. In 2005, Robert et al. [1] combined NanoSIMS element maps with optical microscopic imagery in an effort to develop a new method for assessing biogenicity of Precambrian structures. The ability of NanoSIMS to map simultaneously the distribution of organic elements with a 50 nm spatial resolution provides new biologic markers that could help define the timing of life s development on Earth. The current study corroborates the work of Robert et al. and builds on their study by using NanoSIMS to map C, N (as CN), S, Si and O of both excellently preserved microfossils and less well preserved, non-descript organics in Proterozoic chert from the ca. 0.8 Ga Bitter Springs Formation of Australia.
The mountains of Nepal are among the most hazardous environments in the world, with frequent landslides causing loss of lives and substantial damage to infrastructure every year. Landslide mapping is vital for characterizing impacts in the days after extreme triggering events, as well as for advancing landslide hazard models through robust calibration and validation. Conventionally, landslide inventories are produced by field surveys and/or manual interpretation of aerial photos and satellite images. This process is very subjective and time consuming.
Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.
Kinases are a family of proteins that function as molecular switches, regulating several essential cellular activities such as cell proliferation. Dysfunctional kinases are implicated in several types of cancers and hence they are actively pursued as drug targets. Given the vast number of complex kinase structures that are available in the protein data bank (PDB), there is a necessity to develop methodologies that can identify structurally important moieties of the kinases in an automated fashion, for such techniques can be instrumental in identifying novel drug targets. In this work, we develop a graph neural network (GNN) based deep learning framework for classifying the functionally active and inactive states of a large set of eukaryotic protein kinases, making use of their 3D structure from the PDB. We show that GNN based machine learning models can classify protein states with an accuracy greater than 97%. We further use the GNN models to automatically identify regions of the kinases that are important for its function. For this purpose, Gradient-weighted Class Activation Mapping (Grad-CAM) was implemented on the protein graphs. Remarkably, Grad-CAM consistently identifies the highly conserved DFG motif as the most important part of the protein across the entire kinome, without any prior input. Other regions of the hydrophobic core such as the HRD motif were also identified by the interpretable GNN framework, consistent with the literature. We discuss the significance of each of these regions in detail.
Visible and near-infrared reflectance spectra and multispectral maps of the Martian surface are discussed, and implications of the data for the composition of the Martian surface are considered. Spacecraft and earth-based telescopic observations have confirmed the generally bimodal albedo distribution of the planet, dividing the surface into bright and dark regions. Mars spectra are characterized by the presence of strong Fe(+3) absorption, which is attributed to various ferric oxide minerals. Interpretations of the spectra from the dark regions indicate a basaltic or ultramafic source rock. Evidence for water ice or a highly desiccated metal hydrate has been obtained, along with evidence for CO2-ice only in the south polar cap. Mariner 9 observations of Martian dust suggest the presence of rather acidic rock or mineral particles, or a montmorillonite-type clay. Prospects for the future study of Martian surface composition include continuing earth-based spectrophotometric studies, and high-spectral-resolution mapping of a significant portion of the surface by the Galileo spacecraft and the next Mars mission.
Soil association maps show the spatial relationships of land units developed in unique climatic, geologic, and topographic environments, and having characteristic slopes, soil depths, textures, available water capacities, permeabilities, and the like. From these characteristics of the soil, broad interpretations can be made such as how the soil is suited for various agronomic and engineering uses. ERTS-1 imagery was found to be a useful tool in the identification of soil associations since it provides a synoptic view of an 8 million acre scene, which is large enough so that the effect can be seen on soils of climate, topography, and geology. A regional view also allows soil associations to be observed over most, if not all, of their extent. This aids in selecting typical sampling sites and provides a check on the homogeniety of the associations.
Soil association maps show the spatial relationships of land units developed in unique climatic, geologic, and topographic environments, and having characteristic slopes, soil depths, textures, available water capacities, permeabilities, and the like. From these characteristics of the soil, broad interpretations can be made such as how the soil is suited for various agronomic and engineering uses. ERTS-1 imagery was found to be a useful tool in the identification of soil associations since it provides a synoptic view of an 8 million acre scene, which is large enough so that the effect can be seen on soils of climate, topography, and geology. A regional view also allows soil associations to be observed over most, if not all, of their extent. This aids in selecting typical sampling sites and provides a check on the homogeneity of the associations.
The imagery obtained with the aid of synthetic aperture radars (SAR) has been applied to several remote sensing disciplines such as geologic feature mapping, oceanic phenomena studies, and land use and urban morphology studies. The successful SAR experiment on Seasat and the Shuttle demonstrated the feasibility of global radar mapping at relatively high resolution from a spaceborn platform. The present investigation is concerned with the requirements of and the tradeoffs among several SAR image quality parameters. The results are presented from a survey study concerning the interpretability of a set of SAR images. The data used to generate these images were obtained by the Seasat SAR experiments. Attention is given to image scenes and the simulation experiment, image interpretation survey results, multiple-looks, the number of looks vs resolution, and number of bits vs resolution.
Concerns about environmental effects of large scale deforestation have prompted efforts to map forests over large areas using various remote sensing data and image processing techniques. Basic research on the spectral characteristics of forest vegetation are required to form a basis for development of new techniques, and for image interpretation. Examination of LANDSAT data and image processing algorithms over a portion of boreal forest have demonstrated the complexity of relations between the various expressions of forest canopies, environmental variability, and the relative capacities of different image processing algorithms to achieve high classification accuracies under these conditions. Airborne Imaging Spectrometer (AIS) data may in part provide the means to interpret the responses of standard data and techniques to the vegetation based on its relatively high spectral resolution.
Abstract Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data.
The author has identified the following significant results. An important fault zone, which is strongly suspected of being seismically active, was identified on RBV images, ERTS E-1013-17305 (101, 201, and 301), in northeastern Utah. This fault zone is not shown on the Geological Map of the United States nor on the Tectonic Map of North America. When the epicenters of historic earthquakes and their magnitudes were plotted on an overlay corresponding to the scene, a major earthquake cluster up to magnitude 4.9 was found through which the fault zone passes. This suspected active fault zone runs in a northwest-southwest direction cutting across the Patmos Mountains and the southwestern side of the East Tavaputs Plateau from near the junction of the Colorado River with the Dolores River to and beyond the town of Dragerton, Utah. The fault zone which will subsequently be referred to as the Dragerton fault zone appears to be an element of a major tectonic lineament which includes the Moab fault, Salt Valley, Spanish and Lisbon Valleys. Because of the limited imagery coverage received so far, the extent of this lineament or its tectonic significance cannot be ascertained. It is suspected, however, that it constitutes a major crustal break in the Colorado Plateau.