Results for “process mining” — Page 24

DOE OSTI2021

Critical review of functionalized silica sorbent strategies for selective extraction of rare earth elements from acid mine drainage

We report the ubiquitous and growing global reliance on rare earth elements (REEs) for modern technology and the need for reliable domestic sources underscore the rising trend in REE-related research. Adsorption-based methods for REE recovery from liquid waste sources are well-positioned to compete with those of solvent extraction, both because of their expected lower negative environmental impact and simpler process operations. Functionalized silica represents a rising category of low cost and stable sorbents for heavy metal and REE recovery. These materials have collectively achieved high capacity and/or high selective removal of REEs from ideal solutions and synthetic or real coal wastewater and other leachate source. These sorbents are competitive with conventional materials, such as ion exchange resins, activated carbon; and novel polymeric materials like ion-imprinted particles and metal organic frameworks (MOFs). This critical review first presents a data mining analysis for rare earth element recovery publications indexed in Web of science, highlighting changes in REE recovery research foci and confirming the sharply growing interest in functionalized silica sorbents. A detailed examination of sorbent formulation and operation strategies to selectively separate heavy (HREE), middle (MREE), and light (LREE) REEs from the aqueous sources is presented. Selectivity values for sorbents were largely calculated from available figure data and gauged the success of the associated strategies, primarily: (1) silane-grafted ligands, (2) impregnated ligands, and (3) bottom-up ligand/silica hybrids. These were often accompanied by successful co-strategies, especially bite angle control, site saturation, and selective REE elution. Recognizing the need to remove competing fouling metals to achieve purified REE “baskets,” we highlight techniques for eliminating these species from acid mine drainage (AMD) and suggest a novel adsorption-based process for purified REE extraction that could be adapted to different water systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1994

A fuzzy classifier system for process control

A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

Karr, C. L.↗

NASA NTRS2015

Swamp Works: A New Approach to Develop Space Mining and Resource Extraction Technologies at the National Aeronautics Space Administration (NASA) Kennedy Space Center (KSC)

The first steps for In Situ Resource Utilization (ISRU) on target bodies such as the Moon, Mars and Near Earth Asteroids (NEA), and even comets, involve the same sequence of steps as in the terrestrial mining of resources. First exploration including prospecting must occur, and then the resource must be acquired through excavation methods if it is of value. Subsequently a load, haul and dump sequence of events occurs, followed by processing of the resource in an ISRU plant, to produce useful commodities. While these technologies and related supporting operations are mature in terrestrial applications, they will be different in space since the environment and indigenous materials are different than on Earth. In addition, the equipment must be highly automated, since for the majority of the production cycle time, there will be no humans present to assist or intervene. This space mining equipment must withstand a harsh environment which includes vacuum, radical temperature swing cycles, highly abrasive lofted dust, electrostatic effects, van der Waals forces effects, galactic cosmic radiation, solar particle events, high thermal gradients when spanning sunlight terminators, steep slopes into craters / lava tubes and cryogenic temperatures as low as 40 K in permanently shadowed regions. In addition the equipment must be tele-operated from Earth or a local base where the crew is sheltered. If the tele-operation occurs from Earth then significant communications latency effects mandate the use of autonomous control systems in the mining equipment. While this is an extremely challenging engineering design scenario, it is also an opportunity, since the technologies developed in this endeavor could be used in the next generations of terrestrial mining equipment, in order to mine deeper, safer, more economical and with a higher degree of flexibility. New space technologies could precipitate new mining solutions here on Earth. The NASA KSC Swamp Works is an innovation environment and methodology, with associated laboratories that uses lean development methods and creativity-enhancing processes to invent and develop new solutions for space exploration. This paper will discuss the Swamp Works approach to developing space mining and resource extraction systems and the vision of space development it serves. The ultimate goal of the Swamp Works is to expand human civilization into the solar system via the use of local resources utilization. By mining and using the local resources in situ, it is conceivable that one day the logistics supply train from Earth can be eliminated and Earth independence of a space-based community will be enabled.

Resource Extraction↗

DOE OSTI2021

Computational catalyst discovery: Active classification through myopic multiscale sampling

We report the recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a “best” catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: “What is the set of catalysts that is worth testing experimentally?” Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as “worth investigating” or “not worth investigating” experimentally. Our results show an ~7–16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Improving protonic ceramic electrochemical cell performance via a dual-phase reaction-sintered bilayer electrolyte

Protonic ceramic electrochemical cells (PCCs) are promising energy conversion devices, but their fabrication remains challenging. In particular, the typical electrolytes for PCCs such as BaCe 0.7 Zr 0.1 Y 0.1 Yb 0.1 O 3−δ (7111) and BaCe 0.4 Zr 0.4 Y 0.1 Yb 0.1 O 3−δ (4411) suffer from intrinsic barium evaporation issues during high-temperature sintering. This tendency towards barium loss, combined with their highly refractory nature, leads to a tradeoff between sinterability and chemical stability. To address this tradeoff, we propose a bilayer electrolyte combining layers of 4411 and 7111 materials that is designed to enhance sinterability and conductivity through dual-phase reactive sintering. Our findings demonstrate that the bilayer structure exhibits shrinkage behavior closely matched to that of the fuel electrode substrate, with a higher shrinkage compared to a single-layer 4411 electrolyte. Utilizing this bilayer electrolyte structure, our PCCs achieve a peak power density of 637 mW∙cm −2 in fuel-cell mode and a current density of 1060 mA∙cm −2 at 1.3 V in electrolysis mode at 600 °C. Our PCCs demonstrate high Faradaic efficiency of 83% at 1.3 V and 500 °C. Hybrid distribution of relaxation times (DRT) polarization mapping further reveals that the bilayer structure reduces Ohmic and polarization resistance in both fuel-cell and electrolysis modes.

ceramic processing↗

DOE OSTI2020

Scalable Knowledge Graph Analytics at 136 Petaflop/s

We are motivated by newly proposed methods for data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover how concepts relate to one another. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as one of computing all-pairs shortest paths (APSP), which becomes a significant bottleneck. In this context, we present a new high-performance algorithm and implementation of the Floyd-Warshall algorithm for distributed-memory parallel computers accelerated by GPUs, which we call DSNAPSHOT (Distributed Accelerated Semiring All-Pairs Shortest Path). For our largest experiments, we ran DSNAPSHOT on a connected input graph with millions of vertices using 4, 096nodes (24,576GPUs) of the Oak Ridge National Laboratory's Summit supercomputer system. We find DSNAPSHOT achieves a sustained performance of 136×1015 floating-point operations per second (136petaflop/s) at a parallel efficiency of 90% under weak scaling and, in absolute speed, 70% of the best possible performance given our computation (in the single-precision tropical semiring or “min-plus” algebra). Looking forward, we believe this novel capability will enable the mining of scholarly knowledge corpora when embedded and integrated into artificial intelligence-driven natural language processing workflows at scale.

Kannan, Ramakrishnan {ramki}↗

DOE OSTI2022

Maximizing the Proppant Carrying and Viscoelastic Properties of the Bakken Hypersaline-Produced Water with High-Viscosity Friction Reducers for Sustainable Applications

Summary The development and production of unconventional reservoirs, such as the Bakken Formation, have become a resolved mystery for operators in North America since the arrival and advancement of horizontal drilling and hydraulic fracturing technologies. As a result, unconventional reservoir assets became the central focus of the oil and gas industry at the state, national, and global levels. The produced water from these activities in the Bakken Formation have high salt contents (110,000–350,000 ppm) total dissolve solids (TDS) and can pose significant challenges to the environment if not treated. Deep injection into disposal wells is the routine method used to get rid of the Bakken produced water. However, there have been some concerns that unrestrained injections, in addition to polluting the groundwater, could potentially lead to seismic activities either at the time of injection or in the near future. To diminish the environmental impacts that may be associated with induced seismicity, including the reduction of the costs of water acquisition, the produced water can be treated and reused in the hydraulic fracturing processes. Also, the treated water could be used for irrigation purposes, for power generation, and coal mining operations. The issues of waste water and residual oil high in TDS are challenges yet to be effectively addressed despite preceding research and studies on advancing produced water technologies. The goal of this study is to explore all applicable ways by which the produced water from the Bakken Formation can serve as a replacement base fluid for use with polymers like the high-viscosity friction reducers (HVFRs) to create hydraulic fracturing fluids that can be stable with reservoir conditions and also be able to minimize environmental impacts and cost of operations. Experimental investigations using the high-salinity produced water from the Bakken Formation with HVFRs were carried out. The studies included a base case that served as a bench mark for comparing the effectiveness of the other scenarios. The results indicate that the Bakken hyper-saline produced water can withstand effect of heavy metals, salinity, hardness and remain stable through different shear rates (66–330 s−1) when treated with higher dosages [4–8 gal/1,000 gal (gpt)] of HVFRs. Filtration and dilution were the only methods used on the Bakken Formation produced wate for this research

Engineering↗

NASA NTRS1988

Three-legged mobile platform: SKITTER

Described herein is a system of equipment intended for site preparation and construction of a lunar base. The proximate era of lunar exploration and the initial phase of outpost habitation are addressed. Drilling, mining, and benefication of lunar soil prior to pilot-plant oxygen extraction and other soil processing techniques are included within the scope of this system's capabilities. The centerpiece of the design is the SKITTER mobile walking, site preparation, and construction planform. The SKITTER system is modular in nature and includes a series of interchangeable implements whose individual use tends to be seasonal or intermittent. The concept is analogous to the farmer's tractor and implement set. The proposed system is mechanically simple and weight efficient and the individual implements are designed to take advantage of the lunar environment while operating within the constraints imposed by that environment. The system's implement interfaces can also be used for transporting containerized cargo on the lunar surface. A proposed lunar orbit to lunar surface landing pod might also be used to deliver the walker and crane boom combination in a ready-to-work configuration.

Source record↗

NASA NTRS2008

Moving Away from Ones and Zeros, Designing a Ground Data System Based on Higher Levels of Abstraction

Previous JPL ground systems have been designed with the Ground Data System (GDS) engineer in mind. The focus on these systems has been on packaging and delivery of low level information (frames, packets, telemetry values) to the end user. It was not that long ago when project teams would be huddled over a workstation, examining crude displays of telemetry bits organized in various ways, trying to determine the status of a spacecraft. Understanding the data often required additional levels of GDS expertise, or worse, transformation of the raw data into alternative formats followed by ingestion into other tools so that the data became meaningful. The primary focus was often to answer these types of questions: "Why did this particular frame fail Reed-Solomon decode? Why did this packet get marked as invalid? Why am I missing a block of telemetry from my query?" -- which are completely valid questions to ask from a GDS Engineer's point of view, and large families of tools have been designed to help answer these questions. But these are not the questions that most users care about - which are more like: "Why is the battery state of charge trending down? Show me a summary image report for the last traverse to the target. Show me a data accountability summary for the last DSN pass." Answers to these questions, which are what users are looking for, requires a higher level of abstraction and supporting tools than mining through ones and zeros. JPL has created a next generation capability called the Mission Data Processing and Control System (MPCS) which is designed to support this higher level of abstraction by providing customizable views of the ground system combining collections of lower level information into more meaningful ways. Instead of examining frames, packets, and individual telemetry data points -- MPCS is capable of providing comprehensive summary reports, product status, overall flight/ground event status, as well as payload health summaries. Based on these higher level views, end users can make tactical or strategic decisions, or drop into detailed analysis as needed. System designers need to continue building systems that support low level GDS troubleshooting - but the basic design of a GDS should be geared towards what end users actually need to see. This paper will describe the capabilities of MPCS that directly support these higher levels of abstraction, and which are being used today in missions such as the Mars Science Laboratory and other NASA missions.

MPCS↗

NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗

NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗

NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗

NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

open access↗

NASA NTRS2009

Progress of the NASA/USGS Lunar Regolith Simulant Project

Beginning in 2004 personnel at MSFC began serious efforts to develop a new generation of lunar simulants. The first two products were a replication of the previous JSC-1 simulant under a contract to Orbitec and a major workshop in 2005 on future simulant development. Beginning in 2006 the project refocused its efforts and approached simulant development in a new and more comprehensive manner, examining new approaches in simulant development and ways to more accurately compare simulants to actual lunar materials. This led to a multi-year effort with five major tasks running in parallel. The five tasks are Requirements, Lunar Analysis, Process Development, Feed Stocks, and Standards. Major progress has been made in all five areas. A substantial draft of a formal requirements document now exists and has been largely stable since 2007. It does evolve as specific details of the standards and Lunar Analysis efforts proceed. Lunar Analysis has turned out to be vastly more difficult than anticipated. After great effort to mine existing published and gray literature, the team has realized the necessity of making new measurements of the Apollo samples, an effort that is currently in progress. Process development is substantially ahead of expectations in 2006. It is now practical to synthesize glasses of appropriate composition and purity. It is also possible to make agglutinate particles in significant quantities. A series of minerals commonly found on the Moon has been synthesized. Separation of mineral constituents from starting rock material is also proceeding. Customized grinding and mixing processes have been developed and tested are now being documented. Identification and development of appropriate feedstocks has been both easier and more difficult than anticipated. The Stillwater Mining Company, operating in the Stillwater layered mafic intrusive complex of Montana, has been an amazing resource for the project, but finding adequate sources for some of the components remains a difficult problem. For example the ratio of clino- to ortho-pyroxenes in the Stillwater is not an exact match for lunar materials. One of the sources being examined as an alternative pyroxene source is the Bushveld Complex in South Africa. Standards have been a major success for the project. The Figure of Merit algorithms have been created, tested, and are being considered for an ISO standard. Agreement has been reached in the community about how to make many of the critical measurements. There remains much work to do: (1) driving down the cost of simulants remains a major obstacle; (2) documentation and cost data analysis have not kept up with progress; (3) educating users in the complexity of the lunar regolith and the use of simulants remains a major task. In summary the project has made enormous progress and is successfully placing simulant development and use on a rigorous, scientifically defensible, engineering basis.

Rickman, Doug↗

NASA NTRS2011

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykhian, Gholam Ali↗

NASA NTRS2011

Improve Data Mining and Knowledge Discovery through the use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykahian, Gholan Ali↗

DOE OSTI2024

A shift between mineral and nonmineral sources of iron and sulfur causes proteome-wide changes in Methanosarcina barkeri

ABSTRACT Iron (Fe) and sulfur (S) are required elements for life, and changes in their availability can limit the ecological distribution and function of microorganisms. In anoxic environments, soluble Fe typically exists as ferrous iron [Fe(II)] and S as sulfide (HS − ). These species exhibit a strong affinity that ultimately drives the formation of sedimentary pyrite (FeS 2 ). Recently, paradigm-shifting studies indicate that Fe and S in FeS 2 can be made bioavailable by methanogens through a reductive dissolution process. However, the impact of the utilization of FeS 2 , as opposed to canonical Fe and S sources, on the phenotype of cells is not fully understood. Here, shotgun proteomics was utilized to measure changes in the phenotype of Methanosarcina barkeri MS grown with FeS 2 , Fe(II)/HS − , or Fe(II)/cysteine. Shotgun proteomics tracked 1,019 proteins overall, with 307 observed to change between growth conditions. Functional characterization and pathway analyses revealed these changes to be systemic and largely tangential to Fe/S metabolism. As a final step, the proteomics data were viewed with respect to previously collected transcriptomics data to deepen the analysis. Presented here is evidence that M. barkeri adopts distinct phenotypes to exploit specific sources of Fe and S in its environment. This is supported by observed protein abundance changes across broad categories of cellular biology. DNA adjacent metabolism, central carbon metabolism methanogenesis, metal trafficking, quorum sensing, and porphyrin biosynthesis pathways are all features in the phenotypic differentiation. Differences in trace metal availability attributed to complexation with HS − , either as a component of the growth medium [Fe(II)/HS − ] or generated through reduction of FeS 2 , were likely a major factor underpinning these phenotypic differences. IMPORTANCE The methanogenic archaeon Methanosarcina barkeri holds great potential for industrial bio-mining and energy generation technologies. Much of the biochemistry of this microbe is poorly understood, and its characterization will provide a glimpse into biological processes that evolved close to life’s origin. The discovery of its ability to extract iron and sulfur from bulk, solid-phase minerals shifted a longstanding paradigm that these elements were inaccessible to biological systems. The full elucidation of this process has the potential to help scientists and engineers extract valuable metals from low-grade ore and mine waste generating energy in the form of methane while doing so.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1999

Lunar Polar Ice: Methods for Mining the New Resource for Exploration

The presence of ice in permanently shadowed depressions near the lunar poles and determination of its properties will significantly influence both the near- and long-term prospects for lunar exploration and development. Since data from the Lunar Prospector spacecraft indicate that water ice is likely present (the instrument measures hydrogen strongly suggests the presence of water), it is important to understand how to extract it for beneficial use, as well as how to preserve it for scientific analysis. Two types of processes can be considered for the extraction of water ice from the lunar poles. In the first case, energy is transported into the shadowed regions, ice is constrain models of impacts on the lunar surface and processed in-situ, and water is transported out of the cold trap. In the second case, ice-containing regolith can be mined in the cold trap, transported outside the cold trap, and the ice extracted in a location with abundant solar energy. A series of conceptual implementations has been examined and criteria have been developed for the selection of systems and subsystems for further study.

Gustafson, Robert J.↗