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- Objective - Background - Capability Resource Tables (CRT) - Root Cause Analysis(RCA) - Error types - RCA - Fishbone; 5 whys - Lessons Learned
The Memphis Aquifer (MA) is located in the Mississippi Embayment that extends 250,000 square kilometers across eight states. Fayette and Haywood counties in West Tennessee are situated within the recharge zone of the MA and include the forthcoming Ford “mega campus” named Blue Oval City (BOC), which will consist of a vehicle-production facility and battery assembly division. Increased water demand and land cover change resulting from urban development, such as BOC in the MA’s narrow recharge zone, threaten the aquifer’s groundwater storage and recharge rate. Groundwater recharge factors that influence the narrow recharge zone of the MA include precipitation, evapotranspiration, runoff, and land cover type. In partnership with Protect Our Aquifer (POA) and the Center for Applied Earth Science and Engineering Research (CAESAR) at the University of Memphis, the team used data from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM IMERG), and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). The team also used ancillary data from the National Land Cover Database (NLCD) and the North American Land Data Assimilation System (NLDAS) Noah Land Surface Model. These results identified “thriving” recharge locations, which are areas most conducive to aquifer recharge in Fayette County. The partners may use the results to prioritize specific areas in need of protection before they become susceptible to the effects of urbanization and industrialization.
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As supercomputing resources increase, cosmological scientists are able to run more detailed and larger simulations generating massive amounts of data. Analyzing these simulations with an available open-source toolkit is important for collaborative Department of Energy scientific discovery across labs, universities, and other partners. Developed software as a part of this collection include: comparing data with other existing simulations, verifying and validating results with observation databases, new halo finder algorithms, and using analytical tools to get insights into the physics of the cosmological universe. The goal of this software project is to provide a set of open-source libraries, tools, and packages for large-scale cosmology that allows scientists to visualize, analyze, and compare large-scale simulation and observational data sets. Developed software will provide a variety of methods for processing, visualization, and analysis of astronomical observation and cosmological simulation data. These tools are intended for deployment on multiple scientific computing platforms, including but not limited to personal computers, cloud computing, experimental sites (telescopes) and high performance supercomputers.
This report is a deliverable for a project sponsored by the U.S. Department of Energy (DOE) entitled National Offshore Wind Energy Resource and Design Data Campaign -- Analysis and Collaboration (contract number DE-EE0005372; prime contractor -- AWS Truepower). The project objective is to supplement, facilitate, and enhance ongoing multiagency efforts to develop an integrated national offshore wind energy data network. The results of this initiative are intended to 1) produce a comprehensive definition of relevant met-ocean resource assets and needs and design standards, and 2) provide a basis for recommendations for meeting offshore wind energy industry data and design certification requirements.
Today, the importance of nuclear power in the US is a geopolitical matter as much as it is an economic one. This is due to the notion that increasing the use of nuclear power reduces the US's reliance on imported oil and gas. In order to design future systems and continue operational improvements at existing US plants, advanced modeling and simulation of nuclear power reactors is crucial. A variety of challenges exist to using advanced modeling and simulation in practice, especially for the nuclear energy community and other organizations with fewer computational resources and limited expertise. There is an increasing need of access to highly capable compute, data, and software resources from network systems, which are becoming more administratively restricted from access through installation of traditional client-server applications. The nuclear energy community is not capable of maintaining exponential growth of computing capability and data storage coupled with the necessary end-to-end network bandwidth improvements. Applications that provide higher fidelity with increased physics and decreased assumptions, by their very nature, require more and more computational resources. We believe Web-based, Cloud-enabled, end-to-end simulation software for nuclear energy advanced modeling and simulation to be the solution to these challenges.
Standardization and best practices of data sets and models enable the industry to develop widely accepted protocols for various stages of solar project development and operations. In collaboration with the International Energy Agency Photovoltaic Power Systems Programme Task 16, the National Renewable Energy Laboratory (NREL) developed and regularly updates the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications. In collaboration with ASTM members on the radiometry subcommittee, NREL assists in and leads the development of radiometric standards that contribute to increased accuracy in the measurement of solar resources available to conversion systems and to the development and improvement of models. These models can then provide long-term geographic distribution of solar resources for areas where measured solar data are not available. Further, these standards play a preeminent role in all aspects of solar energy projects, including standard conditions, methods and instrumentation, accelerated testing, and service lifetime of materials.
The need for a realistic set of earth resources collection requirements to test and maximize the data gathering capabilities of the EOS remote sensor systems is considered. The collection requirements will be derived from established user requirements. In order to confine and bound the requirements study, some baseline assumptions were established. These are: (1) image acquisition is confined to the contiguous United States, (2) the fundamental data users are select participating federal agencies, (3) the acquired data will be applied to generating information necessary or in support of existing federal agency charters, and (4) the most pressing or desired federal agency earth resources data requirements have been defined, suggested, or implied in current available literature.
During 2005 through 2008, NASA defined and implemented a major evolutionary change in it Earth Observing system Data and Information System (EOSDIS) to modernize its capabilities. This implementation was based on a vision for 2015 developed during 2005. The EOSDIS 2015 Vision emphasizes increased end-to-end data system efficiency and operability; increased data usability; improved support for end users; and decreased operations costs. One key feature of the Evolution plan was achieving higher operational maturity (ingest, reconciliation, search and order, performance, error handling) for the NASA s Earth Observing System Clearinghouse (ECHO). The ECHO system is an operational metadata registry through which the scientific community can easily discover and exchange NASA's Earth science data and services. ECHO contains metadata for 2,726 data collections comprising over 87 million individual data granules and 34 million browse images, consisting of NASA s EOSDIS Data Centers and the United States Geological Survey's Landsat Project holdings. ECHO is a middleware component based on a Service Oriented Architecture (SOA). The system is comprised of a set of infrastructure services that enable the fundamental SOA functions: publish, discover, and access Earth science resources. It also provides additional services such as user management, data access control, and order management. The ECHO system has a data registry and a services registry. The data registry enables organizations to publish EOS and other Earth-science related data holdings to a common metadata model. These holdings are described through metadata in terms of datasets (types of data) and granules (specific data items of those types). ECHO also supports browse images, which provide a visual representation of the data. The published metadata can be mapped to and from existing standards (e.g., FGDC, ISO 19115). With ECHO, users can find the metadata stored in the data registry and then access the data either directly online or through a brokered order to the data archive organization. ECHO stores metadata from a variety of science disciplines and domains, including Climate Variability and Change, Carbon Cycle and Ecosystems, Earth Surface and Interior, Atmospheric Composition, Weather, and Water and Energy Cycle. ECHO also has a services registry for community-developed search services and data services. ECHO provides a platform for the publication, discovery, understanding and access to NASA s Earth Observation resources (data, service and clients). In their native state, these data, service and client resources are not necessarily targeted for use beyond their original mission. However, with the proper interoperability mechanisms, users of these resources can expand their value, by accessing, combining and applying them in unforeseen ways.
The author has identified the following significant results. A computerized procedure has been developed to correlate coordinates from topographic maps and/or aerial photographs with ERTS data coordinates. Application to data from other multispectral scanners is anticipated. In the procedure, a map transformation from earth coordinates to ERTS point and line numbers is calculated using selected ground control points and the method of least squares. The map transformation is then applied to the earth coordinates of selected areas to obtain the corresponding ERTS point and line numbers. An optional provision allows moving the boundaries of the plots inwards by variable distances (typically half a resolution element) so the selected pixels will not overlap adjacent features.
Large simulation data requires a lot of time and computational resources to compute, store, analyze, visualize, and run user studies. Today, the largest cost of a supercomputer is not hardware but maintenance, in particular energy consumption. Our goal is to balance energy consumption and cognitive value of visualizations of resulting data. This requires us to go through the entire processing pipeline, from simulation to user studies. To reduce the amount of resources, data can be sampled or compressed. While this adds more computation time, the computational overhead is negligible compared to the simulation time. We built a processing pipeline at the example of regular sampling. The reasons for this choice are two-fold: using a simple example reduces unnecessary complexity as we know what to expect from the results. Furthermore, it provides a good baseline for future, more elaborate sampling methods. We measured time and energy for each test we did, and we conducted user studies in Amazon Mechanical Turk (AMT) for a range of different results we produced through sampling.
The state-of-art satellite observations of atmospheric aerosols over the last two decades from NASA's MODIS instruments have been extensively utilized in climate change and air quality research and applications. The operational algorithms now produce level 2 aerosol data at varying spatial resolutions (1, 3, and 10 km) and level 3 data at 1 degree. The local and global applications have been benefited from the coarse resolution gridded data sets (i.e., level 3, 1 degree), as it is easier to use since data volume is low and, several online and offline tools are readily available to access and analyze the data with minimal computing resources. At the same time, researchers who require data at much finer spatial scales have to go through a challenging process of obtaining, processing, and analyzing larger volumes of data sets that require high-end computing resources and coding skills. Therefore, we have created a high spatial resolution (HRG, 0.1x0.1 degree) daily and monthly aerosol optical depth (AOD) product by combining two MODIS operational algorithms, namely Deep Blue (DB) and Dark Target (DT). The new HRG AODs meets the accuracy requirements of level 2 AOD data and provide either the same or more spatial coverage on daily and monthly scales. The data sets are provided in daily and monthly files through open Ftp server with python scripts to read and map the data. The reduced data volume with an easy to use format and tools to access the data will encourage more users to utilize the data for research and applications.
Deep learning has surged in popularity and proven to be effective for various artificial intelligence applications including information extraction from cancer pathology reports. Since word representation is a core unit that enables deep learning algorithms to understand words and be able to perform NLP, this representation must include as much information as possible to help these algorithms achieve high classification performance. Therefore, in this work in addition to the distributional information of words in large sized corpora, we use UMLS vocabulary resources to enrich the vector space representation of words with the semantic relations between words. These resources provide many terminologies pertaining to cancer. The refined word embeddings are used with a convolutional neural (CNN) model to extract four data elements from cancer pathology reports; ICD-O-3 tumor topography codes, tumor laterality, behavior, and histological grade. We observed that using UMLS vocabulary resources to enrich word embeddings of CNN models consistently outperformed CNN models without pre-training word embeddings and even with pre-trained word embeddings on a domain specific corpus across all four tasks. The results show marginal improvement on the laterality task, but a significant improvement on the other tasks, especially for the macro-f score. Specifically, the improvements are 3%, 13%, and 15% for tumor site, histological grade, and behavior tasks, respectively. This approach is encouraging to enrich word embeddings with more clinical data resources to be used for information abstraction tasks from clinical pathology reports.
This article reports the findings of a scoping review assessing the extent and ways in which migrants have been included in health impact assessments (HIAs) and HIA evaluations worldwide. A total of 117 HIAs and two HIA evaluations were included. Only 14% of hand-searched HIAs mentioned migrants, 5% analysed migrants and only 2% included them in their recommendations. Nonetheless, migrants would be expected to be part of the analysis based on the reasons for which migrants were most commonly mentioned. Although the majority of HIAs included in the review mentioned migrants in baseline conditions and impact analysis steps, migrants were seldom included in recommendations. Furthermore, the use of frameworks or tools guiding the completion of an HIA was negatively associated with the inclusion of migrants in recommendations. This is a pivotal risk of frameworks not mentioning migrants. Although workshops and stakeholder engagement were a frequent way of including migrants in HIAs, this usually involved organizations representing migrants, and only seldom included members of the migrant community themselves. The main barriers to including migrants in the HIA impact analysis were the lack of available data on migrants and the significant additional resources required to gather and analyse additional data on migrants. Guidance is needed on ways to optimally include migrants in HIAs and ensure that recommendations for mitigation measures are optimal. - Highlights: • Scoping review found 14% of hand-searched HIAs mentioned migrants • Migrants are seldom mentioned in recommendations even when analysed • Guiding frameworks can hinder the inclusion of migrants if not explicitly mentioned • Often migrants organizations are the ones included in engagement • Main barriers to including migrants are available data and resources required.
Standardization and best practices of data sets and models enable the industry to develop widely accepted protocols adapted to various stages of solar project development and operations. In collaboration with the International Energy Agency Photovoltaic Power Systems Programme Task 16, the National Renewable Energy Laboratory (NREL) developed and regularly updates the Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications. In collaboration with ASTM's radiometry subcommittee, NREL leads the development of various radiometric standards. These contribute to increased accuracy in the measurement of the solar resource available to conversion systems and boost the development and improvement of radiative models. These models can then provide the long-term geographic or climatic distribution of solar radiation for areas where measured solar data are not available. Further, these standards play a preeminent role in all aspects of solar energy projects, including standard conditions, methods and instrumentation, accelerated testing, and service lifetime of materials.
As Artificial Intelligence (AI) becomes more pervasive in our society, it is crucial to develop, deploy, and assess Responsible and Trustworthy AI (RTAI) models, i.e., those that consider not only accuracy but also other aspects, such as explainability, fairness, and energy efficiency. Workflow provenance data have historically enabled critical capabilities towards RTAI. Provenance data derivation paths contribute to responsible workflows through transparency in tracking artifacts and resource consumption. Provenance data are well-known for their trustworthiness helping explainability, reproducibility, and accountability. However, there are complex challenges to achieve RTAI, which are further complicated by the heterogeneous infrastructure in the computing continuum (Edge-Cloud-HPC) used to develop and deploy models. As a result, a significant research and development gap remains between workflow provenance data management and RTAI. In this paper, we present a vision of the pivotal role of workflow provenance in supporting RTAI and discuss related challenges. We present a schematic view between RTAI and provenance, and highlight open research directions.
Abstract FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/
Here, a review of current trends in scientific computing reveals a broad shift to open-source and higher-level programming languages such as Python and growing career opportunities over the next decade. Open-source modeling tools accelerate innovation in equation-based and data-driven applications. Significant resources have been deployed to develop data-driven tools (PyTorch, TensorFlow, Scikit-learn) from tech companies that rely on machine learning services to meet business needs while keeping the foundational tools open. Open-source equation-based tools such as Pyomo, CasADi, Gekko, and JuMP are also gaining momentum according to user community and development pace metrics. Integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to radically accelerate progress. However, long-term support mechanisms are still necessary to sustain the momentum and maintenance of critical foundational packages.