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Intersections of Disadvantaged Communities and Renewable Energy Potential: Data Set and Analysis to Inform Equitable Investment Prioritization in the United States
Renewable energy development can bolster local economies through job creation, local tax revenues, and reduced energy costs; however, communities most in need of economic development and employment opportunities often see lower levels of renewable energy deployment. We sought to identify areas where disadvantaged community indicators and high generation potential from cost-effective renewable energy opportunities intersect and deployment could lead to economic development and job creation. This presentation will highlight several of our findings. This research and the associated county-level data set are intended to inform national- and state-level energy-related assistance programs, economic development efforts, and infrastructure programs seeking to prioritize investments in disadvantaged communities.
Abundance-occupancy distributions to prioritize plant core microbiome membership
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CyTRICS Impact-Based Prioritization Process
Cyber Testing for Resilient Industrial Control Systems™ (CyTRICS™) is the Department of Energy’s (DOE’s) program for cybersecurity vulnerability testing, digital subcomponent enumeration, and forensic assessment. CyTRICS leverages best-in-class test facilities and analytic capabilities at six DOE National Laboratories and strategic partnerships with key stakeholders including technology developers, manufacturers, asset owners and operators, and interagency partners. During the program’s development, CyTRICS established a unique methodology for prioritizing digital components within operational technology (OT) and industrial control systems (ICS) in the Energy Sector Industrial Base (ESIB) for cyber vulnerability testing. The CyTRICS prioritization process leverages multiple characteristics of systems, components, and their contextual deployment to calculate a quantification of individual digital components for CyTRICS testing. The initial version of the CyTRICS prioritization process was premised largely upon the impact which could result to an energy sector industrial control system if the digital component under testing was compromised, either through malicious means, faulty engineering, or other modes. CyTRICS has termed this process the “CyTRICS Impact-based Prioritization Process.” This paper describes the factors identified for use in the Impact-based Prioritization process and identifies the rationale for inclusion. During development, three National Laboratories piloted this prioritization process and generated prioritization scores for seven systems. Following the piloting of the process, laboratory subject matter experts (SME) validated that the numerical scores generated by the prioritization process were consistent with their knowledge of the impact that may occur should any of these systems be disrupted. The following document explains how to perform the prioritization process to generate prioritization scores for energy sector systems. After outlining assumptions required to conduct the process, it describes how to identify and elicit data which can be leveraged to evaluate a system and assign numerical values for each factor. The prioritization process uses different weights on different factors; rationale for each weight is included within the paper. Additionally, the paper includes some recommendations for future enhancements to prioritization, including lessons learned from developing and piloting the process. Finally, a comprehensive appendix includes example documents to be leveraged by those looking to execute the prioritization process.
Prioritizing ICS Beachhead Systems for Cyber Vulnerability Testing
Cyber Testing for Resilient Industrial Control Systems™ (CyTRICS™) is the Department of Energy’s (DOE’s) program for cybersecurity vulnerability testing, digital subcomponent enumeration, and forensic assessment. CyTRICS leverages best-in-class test facilities and analytic capabilities at six DOE National Laboratories and strategic partnerships with key stakeholders including technology developers, manufacturers, asset owners and operators, and interagency partners. During the program’s development, CyTRICS established a unique methodology for prioritizing digital components within operational technology (OT) and industrial control systems (ICS) in the Energy Sector Industrial Base (ESIB) for cyber vulnerability testing. The CyTRICS Prioritization Process leverages multiple characteristics of systems, components, and their contextual deployment to calculate a quantification of individual digital components for CyTRICS testing. The initial version of the CyTRICS Prioritization Process was premised largely upon the impact which could result to an industrial control system if the digital component under testing was compromised, either through malicious means, faulty engineering, or other modes. The worldwide compromise of the SolarWinds Orion platform, first reported in December 2020, through malicious interference with the digital patching cycle was a watershed event in cyber supply chain security. The SolarWinds compromised demonstrated the strategic importance of certain types of ubiquitous software, and the ability to generate widespread cybersecurity effects. To address this challenge and as a part of the Department of Energy’s response to the SolarWinds compromise, DOE’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER) directed the National Laboratories to evolve the CyTRICS Prioritization Process methodology to encompass additional factors related to the strategic importance of digital components. CESER directed CyTRICS researchers to identify, characterize, and append strategic factors to the CyTRICS Prioritization Process to provide additional weight to these characteristics. National Laboratory expert researchers identified functionality, distribution, and platform characteristics for digital components in ICS and OT that they assessed would be likely targeted in strategic initial-access cyber attack. CyTRICS has termed these factors “ICS Beachhead Systems,” leveraging a definition first advanced by Schneider Electric, which is intended as a blanket term to encompass digital components, products, and systems in OT. This paper describes the ICS Beachhead Systems identified and the rationale for inclusion. As a next step in the research and refinement process, the National Laboratories will validate this initial set of characteristics against digital components evaluated by the CyTRICS program and current implementation of the CyTRICS Prioritization Process. After validation, CyTRICS researchers will then develop a scoring methodology to generate a quantitative score to assess the degree to which a digital component is characterized as an ICS Beachhead System. Finally, the National Laboratories will append this scoring to the existing CyTRICS Prioritization Process algorithm.
Applying Costs, Risks and Values Evaluation (CRAVE) methodology to Engineering Support Request (ESR) prioritization
Given limited budget, the problem of prioritization among Engineering Support Requests (ESR's) with varied sizes, shapes, and colors is a difficult one. At the Kennedy Space Center (KSC), the recently developed 4-Matrix (4-M) method represents a step in the right direction as it attempts to combine the traditional criteria of technical merits only with the new concern for cost-effectiveness. However, the 4-M method was not adequately successful in the actual prioritization of ESRs for the fiscal year 1995 (FY95). This research identifies a number of design issues that should help us to develop better methods. It emphasizes that given the variety and diversity of ESR's one should not expect that a single method could help in the assessment of all ESR's. One conclusion is that a methodology such as Costs, Risks, and Values Evaluation (CRAVE) should be adopted. It also is clear that the development of methods such as 4-M requires input not only from engineers with technical expertise in ESR's but also from personnel with adequate background in the theory and practice of cost-effectiveness analysis. At KSC, ESR prioritization is one part of the Ground Support Working Teams (GSWT) Integration Process. It was discovered that the more important barriers to the incorporation of cost-effectiveness considerations in ESR prioritization lie in this process. The culture of integration, and the corresponding structure of review by a committee of peers, is not conducive to the analysis and confrontation necessary in the assessment and prioritization of ESR's. Without assistance from appropriately trained analysts charged with the responsibility to analyze and be confrontational about each ESR, the GSWT steering committee will continue to make its decisions based on incomplete understanding, inconsistent numbers, and at times, colored facts. The current organizational separation of the prioritization and the funding processes is also identified as an important barrier to the pursuit of cost-effectiveness. Perhaps the greatest barrier is that, at the working level, KSC's culture is so preoccupied with technical concerns that it seems almost oblivious to any cost concerns, let alone cost-effectiveness concerns. It is recommended that we must urgently begin to change that culture and seek a better balance between these two concerns.
Prioritized LT Codes
The original Luby Transform (LT) coding scheme is extended to account for data transmissions where some information symbols in a message block are more important than others. Prioritized LT codes provide unequal error protection (UEP) of data on an erasure channel by modifying the original LT encoder. The prioritized algorithm improves high-priority data protection without penalizing low-priority data recovery. Moreover, low-latency decoding is also obtained for high-priority data due to fast encoding. Prioritized LT codes only require a slight change in the original encoding algorithm, and no changes at all at the decoder. Hence, with a small complexity increase in the LT encoder, an improved UEP and low-decoding latency performance for high-priority data can be achieved. LT encoding partitions a data stream into fixed-sized message blocks each with a constant number of information symbols. To generate a code symbol from the information symbols in a message, the Robust-Soliton probability distribution is first applied in order to determine the number of information symbols to be used to compute the code symbol. Then, the specific information symbols are chosen uniform randomly from the message block. Finally, the selected information symbols are XORed to form the code symbol. The Prioritized LT code construction includes an additional restriction that code symbols formed by a relatively small number of XORed information symbols select some of these information symbols from the pool of high-priority data. Once high-priority data are fully covered, encoding continues with the conventional LT approach where code symbols are generated by selecting information symbols from the entire message block including all different priorities. Therefore, if code symbols derived from high-priority data experience an unusual high number of erasures, Prioritized LT codes can still reliably recover both high- and low-priority data. This hybrid approach decides not only "how to encode" but also "what to encode" to achieve UEP. Another advantage of the priority encoding process is that the majority of high-priority data can be decoded sooner since only a small number of code symbols are required to reconstruct high-priority data. This approach increases the likelihood that high-priority data is decoded first over low-priority data. The Prioritized LT code scheme achieves an improvement in high-priority data decoding performance as well as overall information recovery without penalizing the decoding of low-priority data, assuming high-priority data is no more than half of a message block. The cost is in the additional complexity required in the encoder. If extra computation resource is available at the transmitter, image, voice, and video transmission quality in terrestrial and space communications can benefit from accurate use of redundancy in protecting data with varying priorities.
Prioritizing vaccination based on analysis of community networks
Abstract Many countries that had early access to COVID-19 vaccines implemented vaccination strategies that prioritized health care workers and the elderly. As barriers to access eased, vaccine prioritization strategies have been relaxed. However, these strategies are still an important tool for decision makers to manage new variants, plan for future booster shots, or stage mass vaccinations. This paper explores the impact of vaccine prioritization strategies using networks that represent communities with different demographics and connectivity. The impact of vaccination is compared to non-medical intervention to reduce transmission. Several sources of uncertainty are considered, including vaccine willingness and mask effectiveness. This paper finds that while prioritization strategies can have a large impact on reducing deaths and peak hospitalization, selecting the best strategy depends on community characteristics and the desired objective. Additionally, in some cases random vaccination performs as well as more targeted prioritization strategies. Understanding these trade-offs is important when planning vaccine distribution.
A Stochastic Multi-Criteria Decision-Making Algorithm for Dynamic Load Prioritization in Grid-Interactive Efficient Buildings
Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of grid-interactive efficient buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners, and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.
Self-Admitted Technical Debt in Scientific Software: Prioritization, Sentiment, and Propagation Across Artifacts
Self-admitted technical debt (SATD) impairs scientific software (SSW), yet its prioritization, sentiment, persistence, and propagation remains underexplored. Understanding how SSW developers express, and address SATD is crucial for improving SSW maintenance, and tooling. This study investigates how SATD types and artifacts in SSW are prioritized, how sentiment relates to urgency, SATD removal and resolution rates, and the extent to which SATD propagates across artifacts. We analyzed nine SSW repositories using a SATD classification model and a semantic embedding-based prioritization heuristic. SATD was examined across multiple artifacts, with sentiment assessed via a fine-tuned transformer. Propagation was traced, priority scores compared to static analysis, and removal and resolution rates quantified. SATD in comments, commits, and pull requests receive higher priority than SATD in issues, with negative sentiment amplifying urgency. Resolution and removal rates lag behind open-source software (OSS) averages. Most SATD remains confined to the originating artifact, but longer propagation chains are rare and correlate with higher priority, highlighting persistent and high impact debt. Prioritization is influenced by artifact type and sentiment, while low removal and resolution rates signal persistent debt. Cross-artifact propagation marks high priority, unresolved SATD, providing empirical guidance for targeted monitoring, review prioritization, and tool supported maintenance in SSW.
Consequence Prioritization Process for Potential High Consequence Events (HCE)
This document describes the process for Consequence Prioritization, the first phase of the Consequence-Driven Cyber-Informed Engineering (CCE) framework. The primary goal of Consequence Prioritization is to identify potential disruptive events that would significantly inhibit an organization’s ability to provide the critical services and functions deemed fundamental to their business mission. These disruptive events, defined as High Consequence Events (HCE), include both events that have occurred or could be realized through an attack of critical infrastructure owner assets. While other efforts have been initiated to identify and mitigate disruptive events at the national security level, such as Presidential Policy Directive 41 (PPD-41), this process is intended to be used by individual organizations to evaluate events that fall below the threshold for a national security. Described another way, Consequence Prioritization considers threats greater than those addressable by standard cyber-hygiene and includes the consideration of events that go beyond a traditional continuity of operations (COOP) perspective. Finally, Consequence Prioritization is most successful when organizations adopt a multi-disciplinary approach, engaging both cyber security and engineering expertise, as in-depth engineering perspectives are required to recognize and characterize and mitigate HCEs. Figure 1 provides a high-level overview of the prioritization process.
Enabling Intelligent Data Downlink Prioritization of In-Situ Observations through Generalizable and Computationally Inexpensive Anomaly Detection
High-fidelity measurements of magnetic fields and other observed properties, such as energetic particle fluxes, are a necessary component to our understanding of the highly dynamic near-Earth space environment. As our desire to study smaller-scale phenomena such as shocks and dipolorizations has increased, we have been driven to take and telemeter measurements at higher cadences. Unfortunately, many missions are unable to downlink all their captured data due to the well-known data transmission bottleneck at the DSN. These missions must then prioritize their high-cadence data such that the most scientifically useful intervals are transmitted. One simple prioritization technique uses the spacecraft position to telemeter data from only the region of interest. Although easy to implement, this method does not leverage the available scientific data and can omit intervals of useful scientific data when they lie outside the region of interest. The Magnetospheric Multiscale Mission (MMS) uses mission-specific parameterization of several data products to automatically prioritize scientifically useful intervals. Then, MMS verifies the automatically selected intervals by having a domain expert manually select intervals for downlink. The overall complexity required by this technique make it prohibitive for deployment on low-cost platforms (i.e., CubeSats) or on future missions featuring large constellations of satellites such as the Geospace Dynamics Constellation (GDC). We present preliminary results for a simple, generic, and data-driven method of downlink prioritization for magnetic field (and other) measurements. Specifically, Principal Components Analysis (PCA) and One-Class Support Vector Machines (OC-SVMs) are used to detect intervals containing anomalous activity, which can then be prioritized for subsequent downlink. The computational simplicity of this algorithm makes it an excellent candidate for implementation on spaceflight hardware, as well as provide generalizability to a broad range of missions and data products. Initial analysis of this technique has been performed using magnetic field measurements from the Magnetospheric Multiscale Mission and CASSIOP, where it automatically identified scientifically interesting intervals containing Alfvén waves and EMIC activity.
Enabling Intelligent Data Downlink Prioritization of In-Situ Observations through Generalizable and Computationally Inexpensive Anomaly Detection
High-fidelity measurements of magnetic fields and other observed properties, such as energetic particle fluxes, are a necessary component to our understanding of the highly dynamic near-Earth space environment. As our desire to study smaller-scale phenomena such as shocks and dipolorizations has increased, we have been driven to take and telemeter measurements at higher cadences. Unfortunately, many missions are unable to downlink all their captured data due to the well-known data transmission bottleneck at the DSN. These missions must then prioritize their high-cadence data such that the most scientifically useful intervals are transmitted. One simple prioritization technique uses the spacecraft position to telemeter data from only the region of interest. Although easy to implement, this method does not leverage the available scientific data and can omit intervals of useful scientific data when they lie outside the region of interest. The Magnetospheric Multiscale Mission (MMS) uses mission-specific parameterization of several data products to automatically prioritize scientifically useful intervals. Then, MMS verifies the automatically selected intervals by having a domain expert manually select intervals for downlink. The overall complexity required by this technique make it prohibitive for deployment on low-cost platforms (i.e., CubeSats) or on future missions featuring large constellations of satellites such as the Geospace Dynamics Constellation (GDC). We present preliminary results for a simple, generic, and data-driven method of downlink prioritization for magnetic field (and other) measurements. Specifically, Principal Components Analysis (PCA) and One-Class Support Vector Machines (OC-SVMs) are used to detect intervals containing anomalous activity, which can then be prioritized for subsequent downlink. The computational simplicity of this algorithm makes it an excellent candidate for implementation on spaceflight hardware, as well as provide generalizability to a broad range of missions and data products. Initial analysis of this technique has been performed using magnetic field measurements from the Magnetospheric Multiscale Mission and CASSIOP, where it automatically identified scientifically interesting intervals containing Alfvén waves and EMIC activity.
Prioritizing Countries by Concern Regarding Access to Weapons of Mass Destruction Materials
This article presents a global prioritization methodology that evaluates the relative risks of non-state actor acquisition of materials that could be used in chemical, biological, radiological, nuclear and high explosive Weapons of Mass Destruction (WMD) from the country’s relevant infrastructure. Prioritization is based on three domains: 1. Assessing relative scale of materials in each country, 2. The country’s corresponding security posture, and 3. The presence of threat actors. The output is a list of countries prioritized from greatest risk to least. Rather than providing an overall 1 to N ranking, however, the results are placed into tiers based upon their natural groupings within the three domains. The countries in the highest tiers are flagged as potential US national security concern; those scoring in the middle and at the bottom are flagged as posing lower US national security concern. A systematic approach assesses each country by leveraging many disciplines, such as risk and decision analysis, as well as expert judgement. A quantitative value model based on Multi-Attribute Value Theory (MAVT) organizes the objectives scoring criteria into a value tree using lessons learned from previous studies, published literature, and expert judgement. The article presents the prioritization categories and corresponding value model scoring criteria to include measurement type, weight, range, and value preference. Country names and data are notional in order to share the details on the underlying methodology and model without identification of actual security risks. A deliberative process addresses factors external to the model and scrutinizes inputs, methodology, model, and results.
Evaluating Supply Prioritization Strategies for Risk-Informed Decision Making in an Arbitrary Gas Network
Supply disruptions and infrastructure failures in natural gas networks present critical challenges to energy reliability and risk-informed planning. This study evaluates two supply prioritization strategies, Maximum Delivery Prioritization (MDP) and Demand-Based Prioritization (DBP), within an arbitrary natural gas network under conditions of supply shortage. Model performance under both strategies is assessed in response to node and edge failure using demand satisfaction metrics, system-wide and localized dependency scores, and geographic information system (GIS)-based spatial analysis. Results show that DBP better preserves supply for high-demand nodes, while MDP offers broader coverage. The underlying network topology plays a critical role in shaping prioritization outcomes. Integrated GIS visualization enhances the interpretability of vulnerability assessments, revealing structurally critical components and localized vulnerabilities. The proposed framework supports scalable, data-driven decision-making for infrastructure planners and engineers, enabling improved disruption recovery and efficiency in constrained natural gas networks. These insights contribute to the development of more robust energy systems capable of withstanding stress and disruptions.
Prioritization methodology for chemical replacement
Since United States of America federal legislation has required ozone depleting chemicals (class 1 & 2) to be banned from production, The National Aeronautics and Space Administration (NASA) and industry have been required to find other chemicals and methods to replace these target chemicals. This project was initiated as a development of a prioritization methodology suitable for assessing and ranking existing processes for replacement 'urgency.' The methodology was produced in the form of a workbook (NASA Technical Paper 3421). The final workbook contains two tools, one for evaluation and one for prioritization. The two tools are interconnected in that they were developed from one central theme - chemical replacement due to imposed laws and regulations. This workbook provides matrices, detailed explanations of how to use them, and a detailed methodology for prioritization of replacement technology. The main objective is to provide a GUIDELINE to help direct the research for replacement technology. The approach for prioritization called for a system which would result in a numerical rating for the chemicals and processes being assessed. A Quality Function Deployment (QFD) technique was used in order to determine numerical values which would correspond to the concerns raised and their respective importance to the process. This workbook defines the approach and the application of the QFD matrix. This technique: (1) provides a standard database for technology that can be easily reviewed, and (2) provides a standard format for information when requesting resources for further research for chemical replacement technology. Originally, this workbook was to be used for Class 1 and Class 2 chemicals, but it was specifically designed to be flexible enough to be used for any chemical used in a process (if the chemical and/or process needs to be replaced). The methodology consists of comparison matrices (and the smaller comparison components) which allow replacement technology to be quantitatively compared in several categories, and a QFD matrix which allows process/chemical pairs to be rated against one another for importance (using consistent categories). Depending on the need for application, one can choose the part(s) needed or have the methodology completed in its entirety. For example, if a program needs to show the risk of changing a process/chemical one may choose to use part of Matrix A and Matrix C. If a chemical is being used, and the process must be changed; one might use the Process Concerns part of Matrix D for the existing process and all possible replacement processes. If an overall analysis of a program is needed, one may request the QFD to be completed.
Prioritizing Scientific Data for Transmission
A software system has been developed for prioritizing newly acquired geological data onboard a planetary rover. The system has been designed to enable efficient use of limited communication resources by transmitting the data likely to have the most scientific value. This software operates onboard a rover by analyzing collected data, identifying potential scientific targets, and then using that information to prioritize data for transmission to Earth. Currently, the system is focused on the analysis of acquired images, although the general techniques are applicable to a wide range of data modalities. Image prioritization is performed using two main steps. In the first step, the software detects features of interest from each image. In its current application, the system is focused on visual properties of rocks. Thus, rocks are located in each image and rock properties, such as shape, texture, and albedo, are extracted from the identified rocks. In the second step, the features extracted from a group of images are used to prioritize the images using three different methods: (1) identification of key target signature (finding specific rock features the scientist has identified as important), (2) novelty detection (finding rocks we haven t seen before), and (3) representative rock sampling (finding the most average sample of each rock type). These methods use techniques such as K-means unsupervised clustering and a discrimination-based kernel classifier to rank images based on their interest level.
Prioritizing and Managing Technology Needs to Meet River Protection Project Mission Objectives - 16462
Over 56 million gallons of radioactive waste are stored in 177 large underground tanks at the Hanford Site, located in southeastern Washington State. This nuclear waste resulted from plutonium production for the nation's nuclear defense program. The River Protection Project (RPP) mission is to: - Safeguard and safely manage waste stored in the Hanford tanks, - Treat the waste using the Waste Treatment and Immobilization Plant (WTP), - Ensure safe disposition of the waste to protect the Columbia River and the environment. Each aspect of the mission requires some application of technology in order to meet the mission objectives. The RPP Technology Roadmap document presents a comprehensive, integrated assessment of the technology-related advances needed to ensure successful completion of the Hanford tank waste cleanup mission. This paper describes the development of the RPP Technology Roadmap, summarizes its content, describes integration and collaboration with the Department of Energy's national laboratories to support the work identified, and defines how priorities were identified to ensure near-term RPP mission commitments can be accomplished while preserving the longer term technology development elements necessary for mission completion. The Challenge - Managing a wide variety of technology needs that involve multiple organizations, contractors, and national laboratories presents additional challenges when funding constraints are factored in. A process was needed to ensure the technology needs supporting near term mission priorities were appropriately identified, prioritized, and supported through the fiscal year planning process. Additionally, the national laboratory resources necessary to support many of the technologies require coordination to ensure the most appropriate resources are applied at the appropriate level without duplication or contradiction of effort. The Solution - The RPP Technology Roadmap was developed with the primary purposes to: - Identify technology related items needed to support or enhance the River Protection Project (RPP) mission, - Prioritize technology needs to support fiscal year planning processes, - Organize National laboratory support of the RPP mission and communicate needs to the participating contractors. The Technology Roadmap is consistent with Department of Energy (DOE) Hanford mission priorities and is also in alignment with other Hanford Site technical initiatives and policy and planning documents. The Technology Roadmap highlights the current estimated technology needs and identifies where planning shortfalls exist and where national laboratory participation is needed to support mission success. This information assists fiscal year planning and national laboratory contracting processes to ensure that technology needs are supported. In addition to supporting the RPP baseline mission, the Technology Roadmap provides a basis for strategic planning by identifying opportunities to use technology solutions to enhance mission efficiency. This waste management paper describes the innovative and collaborative processes used to develop, validate, and communicate the results so the project owners and the national laboratories can most effectively plan work scope and resources. A discussion of the following processes is included: - Technology needs and task owner identification, - Functional area grouping and area lead identification, - Needs prioritization, - High priority needs ranking (including validation), - Results compilation and planning gap identification, - Results presentation by project and functional area, - National laboratory coordination and collaboration. Application - The process used to develop the RPP technology roadmap is applicable to any site where multiple national laboratories and contractors are involved in identifying and implementing technology based solutions. This process is also applicable to the DOE HQ role to provide prioritization and coordination of the nationwide technology development activities. (authors)