From Cloud to Clarity: Basement Fault Mapping in CO2 Storage Using a Machine Learning-Assisted Workflow
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We estimate the effect of lake water quality on residential housing prices using cross-sectional data on water clarity from 113 lakes across the United States. An instrumental-variables approach is developed to address potential endogeneity bias in the water-clarity variable. Three lake-based physical variables serve as instrumental variables: total nitrogen concentration in lake water, total phosphorus concentration in lake water, and water temperature near the lake surface. The econometric results include three methodological findings: the instruments are valid and strong; the ordinary-least-squares estimate of the coefficient on the water-clarity variable is unbiased; and water clarity is the attribute of water quality valued in the housing market. The estimated water-clarity effect shows that a one-tenth of a meter change in water clarity leads to a one-percent change in housing price, or an elasticity of 0.20 at mean clarity. Coupling this effect with estimates of an ecological production function, a lake-specific benefit index is developed that shows the effect on housing values of bringing lakes into compliance with the US Environmental Protection Agency's regional recommendations for phosphorous concentration in lake waters.
Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m3, followed by PurpleAir PA-II (4.54 μg/m3) and Clarity Node-S (13.68 μg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 μg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 μg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.
Water quality standards in the U.S. consist of: designated uses (the services that a water body provides; e.g., drinking water, aquatic life, harvestable species, recreation) . criteria that define the environmental conditions that must be maintained to support the uses For estuaries and coastal waters in the Gulf of Mexico, there are no numeric (quantitative) criteria to protect designated uses from effects of nutrients. This is largely due to the absence of adequate data that would quantitatively link biological conditions to nutrient concentrations. The Gulf of Mexico Alliance, an organization fostering collaboration between the Gulf States and U.S. Federal agencies, has identified the development of the numeric nutrient criteria as a major step leading to reduction in MODIS Products Figure 6. Map of the Mobile Bay with a yellow patch indicating the Bon Secour Bay area selected in this study for averaging water clarity parameters retrieved from MODIS datasets. nutrient inputs to coastal ecosystems. Nutrient enrichment in estuaries and coastal waters can be quantified based on response variables that measure phytoplankton biomass and water clarity. Long-term, spatially and temporally resolved measurements of chlorophyll a concentration, total concentration of suspended solids, and water clarity are needed to establish reference conditions and to quantify stressor-response relationships.
Bioindicator species monitoring allows researchers to infer the overall ecological health of a given area. Among these species, the Common Loon (Gavia immer), occupies the land-water interface on lakefront habitats in New Hampshire (NH) which exposes nest sites to human encroachment. In collaboration with the Loon Preservation Committee (LPC), we utilized NASA Earth observations to predict future conflicts between loon habitats and human development in New Hampshire. Land use & land cover (LULC), water clarity, and land surface temperature (LST) were analyzed to determine habitat suitability for loons. We used land cover classifications from the National Land Cover Database (NLCD) to analyze development around eight NH lakes (Canobie, First Connecticut, Massabesic, Newfound, Onway, Squam, Umbagog, and Winnipesaukee) from 2001 to 2019. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 5 Thematic Mapper (TM), and Landsat 8 Operational Land Imager (OLI) data provided land surface temperature between 2000 and 2022. Additionally, we utilized Landsat 8 OLI imagery to estimate water clarity between 2013 and 2022. LULC analysis identified areas of development around each lake for 2001, 2019, and land available for future development as of 2019. Assessment of mean summer land surface temperature revealed that loons' presence persists, despite increasing temperatures. We observed increased average water clarity in lakes that were more developed, in general the southernmost lakes analyzed within NH. We synthesized these results to assist the LPC in future conservation efforts.
A pseudo-2d model using COMSOL Multiphysics® software is developed to simulate performance and performance degradation of Li-ion batteries consisting of layered and olivine cathodes with graphite anode when subjected to peak shaving grid service. Multiple degradation pathways are considered, including solid electrolyte interphase (SEI) formation and breakdown at the anode, cathode dissolution and its synergistic effect on SEI formation at the anode. The model is validated by simulating commercial cylindrical cell performance. A global model is developed to simulate performance across all chemistries, along with individual chemistry models using global model parameters as initial values. There is good agreement between these models for various optimization parameters such as SEI equilibrium potential, cathode dissolution exchange current density, solvent diffusivity in the SEI and SEI ionic conductivity. To circumvent time constraints related to the COMSOL model, a 0d global model is developed which fits data well and provides more clarity on differences in cathode dissolution exchange current density. Again, good agreement for various optimization parameters is obtained among the COMSOL global & individual chemistry models and the 0-d model. The lessons learned from the physics-based model is used to develop a top down statistics-based model using current, voltage and anode volumetric change per mole lithium intercalated, along with their interactions as degradation predictors. This model predicts out of sample degradation for multiple grid services and electric vehicle drive cycle with high accuracy and provides the pathway to develop an efficient battery management system combining machine learning and findings from physics-based computationally intensive algorithms.
Machine learning (ML) models have been applied to forecast solar energy; however, they often lack clarity of interpretability and underlying physics. This work addresses such challenges by developing a hierarchy of ML models that gradually introduce predictors to improve the forecast accuracy based on a physics-based framework. Three ML models (ARIMA, LSTM, and XGBoost) are examined and compared with four physics-informed persistence models reported in Part I and the simple persistence model to assess the improvement of different models. The 7-year measurements at the U.S. Department of Energy's Atmospheric Radiation Measurement's Southern Great Plains Central Facility site are used for forecasts and evaluations. The results reveal that the step-by-step introduction of predictors leads to different improvements for models at different hierarchical levels. Comparison of the ML models with persistence models shows that LSTM and XGBoost outperform all the persistence models, with LSTM having the overall best performance; however, ARIMA underperforms the four physics-informed persistence models. This study demonstrates the importance and utility of incorporating physics into ML models in improving forecast accuracy by introducing a hierarchy of physics-based predictors, distinguishing predictor contributions, and enhancing the ML interpretability. The combined use of Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) significantly improves the forecast accuracy compared to using individual irradiances alone because the pair contains more information on cloud-radiation interactions.
The demand for new energy infrastructure is increasing across the United States, but heterogenous permitting processes and embedded requirements across different local jurisdictions can cause project delays, increase “soft costs,” and hinder developer expansion. This study analyzes the variability in local permitting requirements across the U.S. and develops a quantitative approach to describe their clarity and effectiveness in enabling infrastructure project development. By using an Energy Language Model (ELM), a large language model (LLM) for energy technologies, we systematically gathered permitting information from nearly 300 state-, county-, and city-level documents, creating a structured dataset of requirements and procedures on an unprecedented scale and speed. Our analysis revealed that local (city and county) permitting requirement documents are underrepresented compared to state-level guidance documents, which can impede timely and cost-effective installation of new electric infrastructure. Our validation process showed that the final database has an accuracy of approximately 95%. We, further, created a new quantitative method to score permitting requirements for clarity and efficiency, with electric vehicle supply equipment as an initial use case. The average local permitting document scored a 1.8 out of 5, which we interpret as meaning that half of the requirements developers face when installing electric infrastructure are ambiguous, increasing both cost and time. We also created a “Generalized Permit Process”, highlighting common procedural steps and identifying specific opportunities for municipalities to improve their documentation. This research establishes a systematic and scalable framework for evaluating the complexities of local infrastructure permitting processes by combining LLM-powered data collection and quantitative scoring. The framework enables policymakers and developers to identify and mitigate procedural bottlenecks, with the expectation that these improvements can accelerate application review and approval, reduce project costs, and expedite connection to utility distribution grids. As a foundational approach for streamlining local project development processes, this study’s methods are intended to be extended to a wide range of energy applications.
AeroShield Materials produces a novel silica aerogel material with exemplary thermal performance and unprecedented optical clarity, offering the potential for super-insulating fenestration and insulated glass to reduce thermal losses in the built environment by billions of dollars every year. One of the most significant challenges facing AeroShield today is the total amount of time that is required to produce large monolithic aerogel samples. This overall process can require as much as 144 hours total, which can significantly hinder scale-up to an economically viable product. The purpose of this Phase 1 research was to continue development of our novel aerogel manufacturing process to reduce material processing time by up to 10x, greatly improving product throughput and reducing cost. AeroShield’s manufacturing process can be divided into 4 major stages, each with their own distinct set of parameters and time requirements. Under this Phase 1 award, AeroShield was able to identify and optimize a number of these parameters, including molding materials, solvent rinse conditions, critical point drying time, and annealing conditions. AeroShield also performed thorough analyses on how these optimized parameters affected important final characteristics of the gels, including optical clarity, thermal conductivity, and dimensional stability. This campaign culminated in the production of laboratory scale aerogel samples using significantly lower process times of both 36 and 18 total hours, which represent Phase 1 Target and Stretch goals. In order to achieve widespread market adoption, monolithic sheets of the aerogel material must be made to industry-standard sizes (8’ x 12’) at a cost that provides 5-7 year or less breakeven energy savings for consumers (<$2 sq/ft). The work performed under this Phase 1 award shows that time and materials required to make aerogel samples, which make up a significant portion of their overall cost, can be greatly reduced without sacrificing quality. AeroShield plans to use these optimized processes to produce larger, product-relevant sized aerogels in order to achieve target final material costs.
The attitude states of an error-state Kalman filter (ESKF) behave differently than most other states in the system due to their multiplicative (rather than additive) nature. One way in which they differ is an error covariance correction step after an ESKF error reset, which is not required for, for example, position and velocity states. This covariance correction step is not intuitive, and it has only been recently derived for coordinate transform matrices. The author of this memo, however, found the provided derivation in [1] confusing due to a lack of clarity surrounding the invoked reference frames, and clarity is required as there are at least 4 different ways to parameterize small-angle attitude errors in an ESKF. Furthermore, while reproducing the work, the author of this memo found a more straightforward derivation that provides additional insight into the correction step. This memo offers a derivation of the attitude error covariance correction step of an ESKF, which pays specific attention to the coordinate reference frames.
The oxygen system cleaning specifications drawn from 23 industrial and government sources are presented along with cleaning processes employed for meeting these specifications, and recommended postcleaning inspection procedures for establishing the cleanliness achieved. Areas of agreement and difference in the specifications, procedures, and inspection are examined. Also, the lack of clarity or specificity will be discussed. This absence of clarity represents potential safety hazards due to misinterpretation. It can result in exorbitant expenditures of time and money in satisfying unnecessary requirements.
A durable antistatic coating is achieved on polymethylmethacrylate plastic without affecting its optical clarity by applying to the surface of the plastic a low molecular weight solvent having a high electron affinity and a high dipole moment, such as acentonitrile or nitromethane alone or in the presence of photopolymerizable monomer. The treated polymethylmethacrylate plastic dissipates most of the induced electrostatic charge and retains its optical clarity. The antistatic behavior persists after washing, rubbing and vacuum treatment.
Using satellite remote sensing data to measure low concentrations of suspended solids in lakes and oceans requires careful evaluation of background signals from the atmosphere and the water surface. Typical background corrections for Lake Superior are presented and the spectral distribution of the residual radiance from three major categories of turbidity in the lake are determined. The results indicate that for large bodies of water, some general information on atmospheric scattering, water clarity, and the optical properties of suspended solids allows estimates of concentrations of suspended solids to within + or - 0.5 mg/L without using real time ground truth data. Under calibrated conditions the threshold detection level is 0.3 mg/L for the fine particulates dispersed throughout the lake and 1 mg/L for the highly light absorbing effluent from rivers. Comparisons of the minimum reflectance over the open lake areas with reflection from the highly absorbing tannin water from rivers provides a check on the clarity of the atmosphere and the excessive background scatter from the water surface.
The primary responsibility of all scientists is to ensure the integrity of their work. For cognitive and social scientists, this means first and foremost preserving clarity about what we know about people, and not allowing descriptions of technology to demean or obscure the reality of how people think, behave, and live. Without this clarity, engineering requirements analyses, tool design, and evaluations of people will be confused. A sharp, uncompromising understanding about the nature of people is essential if we are to design and fit new technologies that are appropriate and successful for NASA's mission operations.
At the request of NASA's Science Mission Directorate (SMD), the National Research Council's (NRC's) Space Studies Board (SSB) initiated a study to review a draft of the SMD's 2014 Science Plan. The request for this review was made at a time when NASA is engaged in the final stages of a comprehensive, agency-wide effort to develop a new strategic plan and at a time when NASA's budget is under considerable stress. SMD's Science Plan serves to provide more detail on its four traditional science disciplines-astronomy and astrophysics, solar and space physics (also called heliophysics), planetary science, and Earth remote sensing and related activities-than is possible in the agency-wide Strategic Plan. In conducting its review of the draft Science Plan, the Committee on the Assessment of the NASA Science Mission Directorate 2014 Science Plan was charged to comment on the following specific areas: (1) Responsiveness to the NRC's guidance on key science issues and opportunities in recent NRC reports; (2) Attention to interdisciplinary aspects and overall scientific balance; (3) Identification and exposition of important opportunities for partnerships as well as education and public outreach; (4) Integration of technology development with the science program; (5) Clarity on how the plan aligns with SMD's strategic planning process; (6) General readability and clarity of presentation; and (7) Other relevant issues as determined by the committee. The main body of the report provides detailed findings and recommendations relating to the draft Science Plan. The highest-level, crosscutting issues are summarized here, and more detail is available in the main body of the report.
It is common practice to write English-language "shall" statements to embody detailed software requirements in aerospace software applications. This paper explores the use of the UML language as a replacement for the English language for this purpose. Among the advantages offered by the Unified Modeling Language (UML) is a high degree of clarity and precision in the expression of domain concepts as well as architecture and design. Can this quality of UML be exploited for the definition of software requirements? While expressing logical behavior, interface characteristics, timeliness constraints, and other constraints on software using UML is commonly done and relatively straight-forward, achieving the additional aspects of the expression and management of software requirements that stakeholders expect, especially traceability, is far less so. These other characteristics, concerned with auditing and quality control, include the ability to trace a requirement to a parent requirement (which may well be an English "shall" statement), to trace a requirement to verification activities or scenarios which verify that requirement, and to trace a requirement to elements of the software design which implement that requirement. UML Use Cases, designed for capturing requirements, have not always been satisfactory. Some applications of them simply use the Use Case model element as a repository for English requirement statements. Other applications of Use Cases, in which Use Cases are incorporated into behavioral diagrams that successfully communicate the behaviors and constraints required of the software, do indeed take advantage of UML's clarity, but not in ways that support the traceability features mentioned above. Our approach uses the Stereotype construct of UML to precisely identify elements of UML constructs, especially behaviors such as State Machines and Activities, as requirements, and also to achieve the necessary mapping capabilities. We describe this approach in the context of a space-based software application currently under development at the Jet Propulsion Laboratory.
The 2024 Software for the NASA Science Mission Directorate Workshop was the first workshop of its kind in over 10 years. The numerous attendees and high level of interaction in this hybrid workshop portrayed the untapped interest and energy in the NASA SMD community about software. Over 100 takeaways were collected from the hosted discussions in four key areas - Communication, Communities, Funding and Clarity - as summarized in this report. One takeaway was representative of all four categories: “Software is not hardware; it is organic and needs a different model. You often don't know which components will be Open-Source reusable until later in the development cycle.” For the communication category, this takeaway motivates the reduction of silos by shifting towards a model that better supports community collaboration on common challenges and a more streamlined and improved software release process. For the communities category, the same statement points to forming communities of practice with varying scopes and a new approach to recognition and incentives for open-source software contributions. Concerning funding, this statement motivates a more sustained and flexible funding model that better supports the software foundation needed for NASA’s long-term success, including the collaboration and infrastructure a good foundation requires. The new approach to clarity motivated by this statement calls for significant changes to the software release process and related policies to streamline compliance, align those policies with the open-source science culture NASA is promoting and with each other, and simplify use of the cloud. It is time to recognize software as a foundational component of NASA with an organic nature not properly supported by current approaches. Different models are needed in all four areas to shift the NASA SMD software community and governance structures into a more efficient, open, and collaborative ecosystem - one that enables ground-breaking science and daring exploration into the coming decades
My project was migrating a Human-Machine Interface (HMI) and Programmable Logic Controller (PLC) program from legacy software to a modern platform, enhancing operational efficiency and eliminating unsupported, obsolete equipment. Initially, I used a migration tool to transfer the programs to the new software, then manually updated variables to align with the new PLC. I optimized the PLC code by leveraging new scaling features in the I/O cards, removing over 45 obsolete rungs, and enhancing clarity with functional tag aliases. Next, I modernized the HMI's visuals with an updated color scheme and 3D buttons. I developed multiple interface versions iteratively refining them based on operator feedback. I also designed and created a dedicated "Rounds" screen incorporating insights from a previous intern’s project to streamline daily rounds. To improve clarity, consistency, and efficiency I redesigned the entire interface. Adding visual indicators to enhance operational awareness: red is used for values outside normal specifications, green for normal operating modes, yellow for manual mode, and grey for offline units. Additionally, I added a dynamic visual representation of tank levels, creating PLC logic to show the level in inches as well as inches and feet. I then incorporated a navigation sidebar to facilitate efficient screen transitions. Risk of human error was mitigated by defining narrow input ranges for tank level shutoff and password protecting setpoint changes. I also fixed issues with datatypes that caused inaccuracies in the original code. Overall, this comprehensive upgrade significantly enhanced the HMI's functionality, user experience, and operational reliability.