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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cyber-Informed Engineering Adoption in University Engineering Programs: An Overview of CIE Integration Successes at Nine U.S. Educational Institutions

This report examines the adoption of Cyber-Informed Engineering (CIE) in university engineering programs, driven by the need to protect critical energy infrastructure from adversarial threats. CIE equips current and future engineers and technicians with the necessary mindset, skills, and competencies to enhance the resilience of engineered systems against cyber attacks. This report highlights nine academic partners who are incorporating CIE into their curricula through various approaches, including lectures, courses, and certificates.

42 ENGINEERING↗
DOE OSTI2022

Efficient Clustering of Software Vulnerabilities using Self Organizing Map (SOM)

The common vulnerabilities and exposures (CVE) database was created with a mission to ``identify, define, and catalog publicly disclosed cybersecurity vulnerabilities''. This rich body of information can be used to enable rapid and efficient response to secure and defend cyber operations and protect critical cyber infrastructure. The main goal of this paper is to develop a visual analytics tool to enable deep analysis of CVEs using unsupervised clustering techniques. We enhance our analysis by first mapping CVEs to hierarchical-classes in Common Weakness Enumeration (CWE) using information in the National Vulnerability Database (NVD). Both the mapping and the numerical representation of CVEs are enabled by V2W-BERT, which uses natural language processing of the extensive information in NVD to generate a large tabular database of 137,226 CVE entries from 1999 to 2020, where each CVE is represented by a vector of 768 numerical features. The vectorized data is processed by Self-Organizing Maps (SOM), which is an unsupervised machine learning technique for dimensionality reduction, visual representation and clustering. Using a Torus map of 6417 units, we achieve ~10-fold data compression of ~140k CVEs using SOM. The trained map is further clustered using standard K-means clustering into 138 clusters of CVEs. We conducted a brief investigation of the rich mapping of CVEs to best-matching-units to K-means clusters, as well as CVEs to CWEs. For example, this novel mapping provided insight into the role of CWE-59 and CWE-264 in several CVEs that is otherwise hard to explore in the original data. We conclude that our this novel approach will not only enable deep analysis of the complex relationships between CVEs and CWEs, but also a mechanism to quickly respond to and design mitigation actions for rapidly evolving vulnerabilities that have not been mapped to existing CWEs.

Panchal, Khyati↗
DOE OSTI2024

Landscape analysis of environmental data sources for linkage with SEER cancer patients database

Abstract One of the challenges associated with understanding environmental impacts on cancer risk and outcomes is estimating potential exposures of individuals diagnosed with cancer to adverse environmental conditions over the life course. Historically, this has been partly due to the lack of reliable measures of cancer patients’ potential environmental exposures before a cancer diagnosis. The emerging sources of cancer-related spatiotemporal environmental data and residential history information, coupled with novel technologies for data extraction and linkage, present an opportunity to integrate these data into the existing cancer surveillance data infrastructure, thereby facilitating more comprehensive assessment of cancer risk and outcomes. In this paper, we performed a landscape analysis of the available environmental data sources that could be linked to historical residential address information of cancer patients’ records collected by the National Cancer Institute’s Surveillance, Epidemiology, and End Results Program. The objective is to enable researchers to use these data to assess potential exposures at the time of cancer initiation through the time of diagnosis and even after diagnosis. The paper addresses the challenges associated with data collection and completeness at various spatial and temporal scales, as well as opportunities and directions for future research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Design of a Novel Information System for Semi-Automated Management of Cybersecurity in Industrial Control Systems

There is an urgent need in many critical infrastructure sectors, including the energy sector, for attaining detailed insights into cybersecurity features and compliance with cybersecurity requirements related to their Operational Technology (OT) deployments. Frequent feature changes of OT devices interfere with this need, posing a great risk to customers. One effective way to address this challenge is via a semi-automated cyber-physical security assurance approach, which enables verification and validation of the OT device cybersecurity claims against actual capabilities, both pre- and post-deployment. To realize this approach, this paper presents new methodology and algorithms to automatically identify cybersecurity-related claims expressed in natural language form in ICS device documents. Here, we developed an identification process that employs natural language processing (NLP) techniques with the goal of semi-automated vetting of detected claims against their device implementation. We also present our novel NLP components for verifying feature claims against relevant cybersecurity requirements. The verification pipeline includes components such as automated vendor identification, device document curation, feature claim identification utilizing sentiment analysis for conflict resolution, and reporting of features that are claimed to be supported or indicated as unsupported. Our novel matching engine represents the first automated information system available in the cybersecurity domain that directly aids the generation of ICS compliance reports.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Cybersecurity Considerations for Emerging Energy Technologies

AI, cloud computing, post-quantum cryptography, zero-trust architectures, microgrids, and virtual power plants. What do these things have in common? They are all emerging technologies in the clean energy space that will be a critical part of grid modernization efforts. As we work towards clean energy and decarbonization targets, these technologies, developed to solve real-world problems, will help us reach goals and achieve new efficiencies as the paradigm of grid operation shifts. However, there are growing concerns about the cybersecurity risks associated with these trending topics as they are used in critical infrastructure applications. This talk will cover gaps, challenges, and opportunities for the secure implementation of grid modernization solutions and novel energy applications of state-of-the-art networking and communications. Proactive risk mitigation strategies, including the application of cyber-informed engineering, will be discussed. Practical applications of these techniques will help provide countermeasures to the impact of cyberattacks on critical infrastructure technologies in a new, digitized grid landscape.

14 SOLAR ENERGY↗
DOE OSTI2020

Assessing the exposure of three diving bird species to offshore wind areas on the U.S. Atlantic Outer Continental Shelf using satellite telemetry

Abstract Aim The United States Atlantic Outer Continental Shelf (OCS) has considerable offshore wind energy potential. Capturing that resource is part of a broader effort to reduce CO 2 emissions. While few turbines have been constructed in U.S. waters, over a dozen currently planned offshore wind projects have the potential to displace marine birds, potentially leading to effective habitat loss. We focused on three diving birds identified in Europe to be vulnerable to displacement. Our research aimed to determine their potential exposure to areas designated or proposed for offshore wind development along the Atlantic OCS. Methods Satellite tracking technology was used to determine the spatial and temporal use and movement patterns of Surf Scoters ( Melanitta perspicillata ), Red‐throated Loons ( Gavia stellata ) and Northern Gannets ( Morus bassanus ), and calculate their exposure to each offshore wind area. We tagged 236 adults in 2012–2015 on the Atlantic OCS from New Jersey to North Carolina; an additional 147 birds tagged in previous tracking studies were integrated into our analyses. Tracking data were analysed in two‐week intervals using dynamic Brownian bridge movement models to develop composite spatial utilization distributions. For each species, these distributions were then used to calculate the spatio‐temporal exposure to each offshore wind area. Results Surf Scoters and Red‐throated Loons were exposed to offshore wind areas almost exclusively during migration because these species were distributed among coastal and inshore waters during winter months. In contrast, Northern Gannets ranged over a much larger area, reaching farther offshore and south in winter, thus exhibited the greatest exposure to extant offshore wind areas. Conclusions Results of this study provide better understanding of how diving birds use current and potential future offshore wind areas on the Atlantic OCS, and can inform permitting, risk assessment and pre‐ and post‐construction impact assessments of offshore energy infrastructure.

Stenhouse, Iain J.↗
DOE OSTI2025

Energy Efficiency for the Wastewater Industry

This information sheet discusses the technology pillar, Energy Efficiency, as a pathway toward improving wastewater infrastructure sustainability and resiliency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Energy Capture Avenues for the Wastewater Industry

This information sheet discusses the technology pillar, Energy Capture, as a pathway toward improving wastewater infrastructure sustainability and resiliency. To supplement existing literature on current technologies and policies for improving resiliency at wastewater treatment plants, this document aims to accomplish: • Summarize the existing technologies for the capture and integration of renewable energy and energy storage. • Identify challenges, concerns, and limitations associated with the implementation of technologies. • Serve as a repository for leading approaches to energy capture systems for wastewater utilities. • Present case studies and examples of best practices.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

A Play-Based Exploration of CO 2 Storage in the Illinois Basin: Community Baseline Assessment for Carbon Storage Engagement Planning in the Illinois Basin

This report presents a stakeholder mapping analysis conducted as part of the Play-Based Exploration of CO 2 Storage in the Illinois Basin project (DOE Cooperative Agreement DE-FE0032366). The analysis was undertaken to systematically identify, categorize, and characterize stakeholder groups and community conditions relevant to the planning and deployment of carbon capture and storage (CCS) infrastructure across the Illinois Basin. Its purpose is to establish a contextual baseline that informs targeted engagement strategies, supports equitable project planning, and strengthens the social dimensions of CCS site screening.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Data for "Implications of Zoning Ordinances for Rural Utility-Scale Solar Deployment and Power System Decarbonization in the Great Lakes Region".

This dataset includes the Energy Zoning Database and geographic shapefiles used to identify suitable rural areas for utility-scale solar development. Additionally, it contains the input data required for the capacity expansion model, including information on existing electric generators and their associated costs, solar resource potential, and transmission infrastructure data. These datasets collectively support the analysis presented in the paper, ensuring a comprehensive assessment of zoning regulations, land suitability, and the economic and technical feasibility of solar deployment in rural areas.

Owusu-Obeng, Papa Yaw (ORCID:0000000334385183)↗
DOE OSTI2020

Adaptive Neuro Fuzzy Inference System for Cyber-Intrusion Detection in a Smart Grid

The evolution of the power grid has brought increasing deployment of advance metering infrastructure, penetration of intelligent electronic devices, and integration of physical power system components with information and communications technologies. With the fast-expanding connectivity, cyber vulnerabilities arise due to the use of internet-based communication systems. These systems are targets of cyber-intrusions which attempt to disturb the normal power system functions. Traditional intrusion detection algorithms have been developed without an explicit model of the cyber components. In this paper, an algorithm to detect false data injections in the power system is proposed considering both cyber and physical models of the power system. The algorithm is based on an Adaptive Neuro Fuzzy Inference System (ANFIS) which collects information from state variables of the cyber-physical system to meet the performance requirements of the grid. Simulations of the proposed approach using the IEEE 13-bus test system validate the effectiveness of this artificial intelligence-based algorithm.

Adaptive Neuro Fuzzy Inference System (ANFIS), cyb↗
DOE OSTI2026

Cyber-Informed Engineering: Incorporating CIE into Engineering Curricula

Cyber-Informed Engineering (CIE) is an engineering approach that mitigates the consequences of cyber risk to critical infrastructure by integrating engineered controls into system design and operation. CIE-focused education is necessary to prepare future engineers and technicians to understand and mitigate digital risk in modern engineered systems. This session explores how universities can incorporate CIE into their curricula, provides examples of how existing universities are already leveraging CIE in their programs, and highlights resources to support adoption.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Community Planning for Solar: Compiling a Community Solar Action Plan

This document provides an annotated outline for a Community Solar Action Plan. This document is designed to assist community officials, volunteers, and regional planning agency staff in preparing a draft Community Solar Action Plan for a municipality, community, or other jurisdiction. Preparing a Community Solar Action Plan is the fifth step in the Community Planning for Solar process (ag.umass.edu/solarplanning). The plan is intended to be the culmination of the planning process. The contents of the plan will include summaries of information gathered as part of foregoing steps in the process, including completion of a Solar Resource and Infrastructure Assessment, administering of a Community Solar Survey, interpretation of survey results, and review of Solar Financing and Ownership Options and tools. Once the Community Solar Action Plan has been drafted, it should be reviewed by municipal board and commission members and presented at an open community forum. Any important comments or revisions can be incorporated before the final Community Solar Action Plan is approved and implemented. The approved Community Solar Action Plan is a living document that guides community solar planning action over time. As conditions change, new information becomes available, or actions progress over the life of the plan, adjustments may be necessary to maintain the plan's relevance. The Monitoring, Evaluating, and Updating Your Community Solar Action Plan fact sheet (Step 6, Item a) describes some things to consider when updating the Community Solar Action Plan over time.

14 SOLAR ENERGY↗
DOE OSTI2022

Renewable Energy and Efficiency Technologies in Scenarios of U.S. Decarbonization in Two Types of Models: Comparison of GCAM Modeling and Sector-Specific Modeling

Energy system projections from quantitative models inform actions ranging from short-term and local decisions, such as those about technology and infrastructure deployment, and global and long-term negotiations and targets. Computational limits require model designers to balance coverage and resolution (i.e., breadth versus depth). Some models, such as the Global Change Analysis Model (GCAM), represent all energy sources and uses with less resolution than models that focus on a single sector's energy use. GCAM balances global supply and demand of all energy carriers projecting prices using internal calculations for energy sources and costs of greenhouse gas mitigation while capturing interlinkages between the energy system, water, agriculture and land use, the economy, and the climate. This globally comprehensive model was used to frame the Long-Term Strategy of the United States: Pathways to Net-Zero Greenhouse Gas Emissions by 2050, which the White House released in 2021 and has been used to inform national and global economy-wide climate change mitigation discussions and strategy development for decades. Unlike GCAM, sectoral models focus on a portion of the energy sector and with greater detail and resolution. The Regional Energy Deployment System (ReEDS) electricity-sector model, for example, projects electricity system capacity expansion and operation with high-fidelity representation of emerging technologies for deep decarbonization, such as variable renewable energy and energy storage, and integration of these technologies into the electric grid. The Transportation Energy and Mobility Pathway Options (TEMPO) transportation-sector model enables analysis of household choices, with a focus on adoption, charging, and use of electric vehicles. The Scout buildings-sector model supports detailed consideration of the policies and markets that can accelerate the adoption of electrification and energy conservation measures in buildings. Such sector-specific models are instrumental in informing technology research, sectoral planning strategies, and sector-specific aspects of greenhouse gas mitigation strategies in the United States. The integrated multisector and sector-specific modeling approaches represented by GCAM and these sectoral models are complementary. The integrated multisector approach calculates energy pricing and resource allocation within the model, which is important for consistency when future conditions substantially diverge from current conditions in transformative scenarios. The sector-specific approach facilitates representation of granular details across spatial, temporal, technological, and market dimensions that enable exploration of particular interactions and trade-offs. This report presents the results of recent work to explore the differences and trade-offs between these approaches by comparing GCAM with the sector-specific ReEDS, TEMPO, and Scout models. The report compares both model structures and results, and it addresses their potential relevance and applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Geospatial and Information Substitution and Anonymization Tool (GISA)

The Geospatial and Information Substitution and Anonymization Tool (GISA) incorporates techniques for obfuscating identifiable information from point data or documents, while simultaneously maintaining chosen variables to enable future use and meaningful analysis. This approach promotes collaboration and data sharing while also reducing the risk of exposure to sensitive information. GISA can be used in a number of different ways, including the anonymization of point spatial data, batch replacement/removal of user-specified terms from file names and from within file content, and aid with the selection and redaction of images and terms based on recommendations using natural language processing. Version 1 of the tool, published here, has updated functionality and enhanced capabilities to the beta version published in 2023. Please see User Documentation for further information on capabilities, as well as a guide for how to download and use the tool. If there are any feedback you would like to provide for the tool, please reach out with your feedback to edxsupport@netl.doe.gov. Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The Geospatial and Information Substitution and Anonymization Tool (GISA) was developed jointly through the U.S. DOE Office of Fossil Energy and Carbon Management’s EDX4CCS Project, in part, from the Bipartisan Infrastructure Law.

Bipartisan Infrastructure Law↗
DOE OSTI2025

CIE Curriculum Guide (V.2.0)

The Cyber-Informed Engineering (CIE) Curriculum Guide offers a comprehensive framework, guidance, and resources for integrating CIE into university-level engineering programs and related educational activities. The primary goal is to help educators adopt CIE principles into their teaching to produce future engineers and technicians who understand digital risks in modern engineered systems, thereby addressing the nation’s infrastructure resilience needs. This guide outlines practical integration examples, links to resources to accelerate CIE adoption, and shares insights from partner academic institutions on various implementation strategies. CIE is a framework for embedding engineered controls that mitigate the impact of cyber-attacks in any cyber-physical system used in critical energy infrastructure and other sectors. Developed by the U.S. Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER), the National Cyber-Informed Engineering Strategy emphasizes embedding CIE into formal education, training, and credentialing. This guide supports this strategic objective by providing examples of integrating CIE concepts into engineering curricula, from class activities to new courses and certificate programs. The importance of educating cyber-informed engineers is underscored by the evolving cybersecurity threats facing engineered systems. As industrial control systems (ICS) increasingly incorporate digital technologies, the responsibility for security extends to both cyber professionals and engineers. CIE addresses critical gaps in designing and protecting physical systems with digital components against cyber risks, ensuring engineers consider digital risk throughout the engineering design lifecycle. Currently, engineering education does not routinely include cyber-informed principles, highlighting a gap in addressing modern engineering system risks. This guide advocates for updating engineering curricula to include digital risk management as a fundamental element. By doing so, future engineers will be equipped to design resilient systems that mitigate digital risks from the outset. Through this guide, engineering faculty can integrate CIE into their curricula, bridging the gap between digital risk and engineering. This approach prepares a cyber-informed workforce capable of safeguarding the cyber-physical systems crucial to national security and public welfare. By embedding CIE into education and training, institutions can produce engineers and technicians who can effectively mitigate cyber impacts throughout the engineering design lifecycle, resulting in more secure critical infrastructures.

42 - ENGINEERING↗
DOE OSTI2024

Feedback on the Electric Emergency Incident and Disturbance Report, OE-417

The US Department of Energy (DOE) Office of Electricity (OE) mandates requirements for reporting electric emergencies and disruptions in the United States. Specific electric power industry organizations (such as balancing authorities, reliability coordinators, some generating entities, and electric utilities) report this information through the OE-417 Electric Emergency Incident and Disturbance Report via Form OE-417. Entities are required to submit Form OE-417 when at least one of the qualifying 26 criteria is met pursuant to Section 13(b) of the Federal Energy Administration Act of 1974 (Public Law 93-275). Entities are required to report, within 6 hours of the incident, loss of electric service to 50,000 customers or more for 1 hour or longer. The deadline for submitting Form OE-417 depends on the nature of the incident. An updated Form OE-417 Schedule 1 and all of Schedule 2 are both due within 72 hours of the incident to provide complete disruption information. Additionally, a Final Report must be filed within 72 hours of the incident, unless an interim update has been provided. If the incident meets specific criteria, the form must be filed within one hour, six hours, or by the later of 24 hours after the recognition of the incident or by the end of the next business day.1 The ability of DOE to respond quickly to energy emergencies that could affect the nation’s infrastructure and help alleviate or prevent further disruptions depends on the industry’s prompt and complete submission of information. This report summarizes current issues with Form OE-417 and the reporting process and presents recommendations for improvements.

24 POWER TRANSMISSION AND DISTRIBUTION↗