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DOE OSTI2025

Intelligent Manufacturing Support: Specialized LLMs for Composite Material Processing and Equipment Operation

Engineering educational curriculum and standards cover many material and manufacturing options. However, engineers and designers are often unfamiliar with certain composite materials or manufacturing techniques. Large language models (LLMs) could potentially bridge the gap. Their capacity to store and retrieve data from large databases provides them with a breadth of knowledge across disciplines. However, their generalized knowledge base can lack targeted, industry-specific knowledge. To this end, we present two LLM-based applications based on the GPT-4 architecture: (1) The Composites Guide: a system that provides expert knowledge on composites material and connects users with research and industry professionals who can provide additional support and (2) The Equipment Assistant: a system that provides guidance for manufacturing tool operation and material characterization. By combining the knowledge of general AI models with industry-specific knowledge, both applications are intended to provide more meaningful information for engineers. In this paper, we discuss the development of the applications and evaluate it through a benchmark and two informal user studies. The benchmark analysis uses the Rouge and Bertscore metrics to evaluate our models’ performance against GPT-4o. The results show that GPT-4o and the proposed models perform similarly or better on the ROUGE and BERTScore metrics. The two user studies supplement this quantitative evaluation by asking experts to provide qualitative and open-ended feedback about our model’s performance on a set of domain-specific questions. The results of both studies highlight a potential for more detailed and specific responses with the Composites Guide and the Equipment Assistant.

Kapoor, Gunnika [Oak Ridge National Laboratory (OR↗

DOE OSTI2026

Quantum Computing for AI-based Design and Optimization of Electric Motors

Knowledge-based artificial intelligence and hierarchical fuzzy logic offer an interpretable framework for electricvehicle motor preliminary design, but their computational burden grows with linguistic granularity and coupled design-space size. This paper presents a reduced quantum reformulation of the hierarchical fuzzy inference of air-gap flux density, a representative level-one motor-design parameter. Starting from the published electric-vehicle motor-design framework, a three-term fuzzy prototype is constructed from the original inference structure. The reduced model is then reformulated as a modular quantum register-oracle system, in which each hierarchical subrelation is encoded as a block oracle and evaluated through superpositionbased candidate-label testing. The proposed modular quantum formulation reproduces the reduced classical prototype after block fusion. A resource analysis shows that the reduced modular system requires seven qubits per block and twenty-two qubits in a straightforward four-block implementation. Finally, a crossovercomplexity model is derived to identify the regime in which quantum candidate search may become favorable relative to hierarchical fuzzy inference. The results show that no quantum advantage should be claimed for the present one-output reduced benchmark, but that a plausible crossover emerges for larger joint candidate spaces and higher linguistic granularity. The work therefore establishes a technically consistent starting point for future quantum-assisted electric-vehicle motor-design optimization.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗

DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2023

Machine learning guided optimal composition selection of niobium alloys for high temperature applications

Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime.

Mohanty, Trupti (ORCID:0000000342701430)↗

DOE OSTI2025

Atomic layer deposition of nanofilms on porous polymer substrates: Strategies for success

Atomic layer deposition (ALD) is a versatile technique for engineering the surfaces of porous polymers, imbuing the flexible, high-surface-area substrates with inorganic and hybrid material properties. Previously reported enhancements include fouling resistance, electrical conductance, thermal stability, photocatalytic activity, hydrophilicity, and oleophilicity. However, there are many poorly understood phenomena that introduce challenges in applying ALD to porous polymers. In this paper, we address five common challenges and ways to overcome them: (1) entrapped precursor, (2) embrittlement, (3) film fracture, (4) deformation, and (5) pore collapse. These challenges are often interrelated and can exacerbate one another. To investigate these phenomena, we applied various ALD chemistries to porous polymers including polyethersulfone, polysulfone, polyvinylidene fluoride, and polycarbonate track-etched membranes. Reaction-diffusion modeling revealed why certain precursors and processing conditions result in embrittling subsurface material growth, entrapment of unreacted precursors, and nongrowth. We quantify the limits of ALD processing temperatures that are dictated by thermal expansion mismatch and can lead to fractured ALD films. The results herein allow us to make recommendations to avoid, mitigate, or overcome the difficulties encountered when performing ALD and plasma-enhanced ALD on porous polymers. We intend this article to serve as a “lessons learned” guide informed by previous experience to provide a better understanding of the difficulties and limitations of ALD on porous polymers and knowledge-based guidelines for successful depositions. This knowledge can accelerate future research and help experimentalists navigate and troubleshoot as they expose porous polymers to reactive precursor vapors.

36 MATERIALS SCIENCE↗

DOE OSTI2012

An Approach to Evaluate Scientist Support in Abstract Workflows and Provenance Traces

Abstract workflows are useful to bridge the gap between scientists and technologists towards using computer systems to carry out scientific processes. Provenance traces provide evidence required to validate results and support their reuse. Assuming both technologies are based on formal semantics, a knowledge-based system that consistently merges both technologies is useful for scientists that produce data to document their data collecting and transformation processes; it is also useful for scientists that reuse data to assess scientific processes and resulting datasets produced by others. While evaluation of each technology is necessary for a given application, this work discusses their combined evaluation. The claim is that both technologies should complement each other and align consistently to a scientist’s perspective in order to be effective for science. Evaluation criteria are proposed based on lessons learned and exemplified for discussion.

Provenance↗

DOE OSTI2016

Thermal Safety Analyses for the Production of Plutonium-238 at the High Flux Isotope Reactor

There has been a considerable effort over the previous few years to demonstrate and optimize the production of plutonium-238 ( 238 Pu) at the High Flux Isotope Reactor (HFIR). This effort has involved resources from multiple divisions and facilities at the Oak Ridge National Laboratory (ORNL) to demonstrate the fabrication, irradiation, and chemical processing of targets containing neptunium-237 ( 237 Np) dioxide (NpO 2 )/aluminum (Al) cermet pellets. A critical preliminary step to irradiation at the HFIR is to demonstrate the safety of the target under irradiation via documented experiment safety analyses. The steady-state thermal safety analyses of the target are simulated in a finite element model with the COMSOL Multiphysics code that determines, among other crucial parameters, the limiting maximum temperature in the target. Safety analysis efforts for this model discussed in the present report include: (1) initial modeling of single and reduced-length pellet capsules in order to generate an experimental knowledge base that incorporate initial non-linear contact heat transfer and fission gas equations, (2) modeling efforts for prototypical designs of partially loaded and fully loaded targets using limited available knowledge of fabrication and irradiation characteristics, and (3) the most recent and comprehensive modeling effort of a fully coupled thermo-mechanical approach over the entire fully loaded target domain incorporating burn-up dependent irradiation behavior and measured target and pellet properties, hereafter referred to as the production model. These models are used to conservatively determine several important steady-state parameters including target stresses and temperatures, the limiting condition of which is the maximum temperature with respect to the melting point. The single pellet model results provide a basis for the safety of the irradiations, followed by parametric analyses in the initial prototypical designs that were necessary due to the limiting fabrication and irradiation data available. The calculated parameters in the final production target model are the most accurate and comprehensive, while still conservative. Over 210 permutations in irradiation time and position were evaluated, and are supported by the most recent inputs and highest fidelity methodology. The results of these analyses show that the models presented in this report provide a robust and reliable basis for previous, current and future experiment safety analyses. In addition, they reveal an evolving knowledge of the steady-state behavior of the NpO 2 /Al pellets under irradiation for a variety of target encapsulations and potential conditions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2013

Potential scenarios for broadening stakeholder involvement in the implementing geological disposal technology platform

This paper analyses the potential for the involvement of different types of stakeholders in the Implementing Geological Disposal Technology Platform (IGD-TP). This analysis was conducted as part of the InSOTEC project, a three-year (2011- 2014) collaborative research project funded under the 7. Euratom Framework Programme (Grant Agreement nr. 269906). In our analysis, we consider the extent to which the IGDTP's practice as regards to stakeholder involvement matches its discourse, and what potential for improvement exists given its structural organisation as a European Technology Platform (ETPs). Technology Platforms (TPs) can be understood as knowledge networks, deliberately set up to influence (research) policy in a specific domain. We therefore use knowledge networks as a conceptual approach and look at the IGD-TP as a complex network which includes actors, knowledge and practices across different countries, focusing on a very specific topic (i.e. implementing geological disposal). We compare the way different stakeholders are involved in the IGD-TP to the practice of other ETPs, and explore how the IGD-TP is viewed by its members and by outsiders to the platform Applying Callon's framework of knowledge co-production (1999) we come to define different degrees of interaction between science, society and policy in view of defining research and development (R and D) priorities [1]. Subsequently we describe how these interactions could be conceptualised and interpreted for the IGD-TP. The current approach of the IGDTP can be mainly understood as classical model involving mainly expert stakeholders and scientists. Where there seems to be a good representation among IGD-TP members of industry, research institutes, and some members of the academic community this is not the case for other types of stakeholders, such as public authorities or civil society. At this stage, the overall approach of the IGD-TP would seem to restrict the scope of stakeholder involvement, as it narrows participation down to uniquely technology experts, hindering socio-technical manifestations. Our analysis nevertheless shows that there is room for engaging with a broader range of stakeholders in the field of radioactive waste management, if this is the intention of the IGD-TP. However, this would require a commitment to developing a common knowledge base including other stakeholders through a process of mutual adjustment and negotiation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2012

A common path forward for the immersive visualization community

Immersive visualization makes use of the medium of virtual reality (VR) - it is a subset of virtual reality focused on the application of VR technologies to scientific and information visualization. As the name implies, there is a particular focus on the physically immersive aspect of VR that more fully engages the perceptual and kinesthetic capabilities of the scientist with the goal of producing greater insight. The immersive visualization community is uniquely positioned to address the analysis needs of the wide spectrum of domain scientists who are becoming increasingly overwhelmed by data. The outputs of computational science simulations and high-resolution sensors are creating a data deluge. Data is coming in faster than it can be analyzed, and there are countless opportunities for discovery that are missed as the data speeds by. By more fully utilizing the scientists visual and other sensory systems, and by offering a more natural user interface with which to interact with computer-generated representations, immersive visualization offers great promise in taming this data torrent. However, increasing the adoption of immersive visualization in scientific research communities can only happen by simultaneously lowering the engagement threshold while raising the measurable benefits of adoption. Scientists time spent immersed with their data will thus be rewarded with higher productivity, deeper insight, and improved creativity. Immersive visualization ties together technologies and methodologies from a variety of related but frequently disjoint areas, including hardware, software and human-computer interaction (HCI) disciplines. In many ways, hardware is a solved problem. There are well established technologies including large walk-in systems such as the CAVE{trademark} and head-based systems such as the Wide-5{trademark}. The advent of new consumer-level technologies now enable an entirely new generation of immersive displays, with smaller footprints and costs, widening the potential consumer base. While one would be hard-pressed to call software a solved problem, we now understand considerably more about best practices for designing and developing sustainable, scalable software systems, and we have useful software examples that illuminate the way to even better implementations. As with any research endeavour, HCI will always be exploring new topics in interface design, but we now have a sizable knowledge base of the strengths and weaknesses of the human perceptual systems and we know how to design effective interfaces for immersive systems. So, in a research landscape with a clear need for better visualization and analysis tools, a methodology in immersive visualization that has been shown to effectively address some of those needs, and vastly improved supporting technologies and knowledge of hardware, software, and HCI, why hasn't immersive visualization 'caught on' more with scientists? What can we do as a community of immersive visualization researchers and practitioners to facilitate greater adoption by scientific communities so as to make the transition from 'the promise of virtual reality' to 'the reality of virtual reality'.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2012

Anomaly Detection in Multiple Scale for Insider Threat Analysis

We propose a method to quantify malicious insider activity with statistical and graph-based analysis aided with semantic scoring rules. Different types of personal activities or interactions are monitored to form a set of directed weighted graphs. The semantic scoring rules assign higher scores for the events more significant and suspicious. Then we build personal activity profiles in the form of score tables. Profiles are created in multiple scales where the low level profiles are aggregated toward more stable higherlevel profiles within the subject or object hierarchy. Further, the profiles are created in different time scales such as day, week, or month. During operation, the insider s current activity profile is compared to the historical profiles to produce an anomaly score. For each subject with a high anomaly score, a subgraph of connected subjects is extracted to look for any related score movement. Finally the subjects are ranked by their anomaly scores to help the analysts focus on high-scored subjects. The threat-ranking component supports the interaction between the User Dashboard and the Insider Threat Knowledge Base portal. The portal includes a repository for historical results, i.e., adjudicated cases containing all of the information first presented to the user and including any additional insights to help the analysts. In this paper we show the framework of the proposed system and the operational algorithms.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2012

Two stage fluid bed-plasma gasification process for solid waste valorisation: Technical review and preliminary thermodynamic modelling of sulphur emissions

Highlights: Black-Right-Pointing-Pointer We investigate sulphur during MSW gasification within a fluid bed-plasma process. Black-Right-Pointing-Pointer We review the literature on the feed, sulphur and process principles therein. Black-Right-Pointing-Pointer The need for research in this area was identified. Black-Right-Pointing-Pointer We perform thermodynamic modelling of the fluid bed stage. Black-Right-Pointing-Pointer Initial findings indicate the prominence of solid phase sulphur. - Abstract: Gasification of solid waste for energy has significant potential given an abundant feed supply and strong policy drivers. Nonetheless, significant ambiguities in the knowledge base are apparent. Consequently this study investigates sulphur mechanisms within a novel two stage fluid bed-plasma gasification process. This paper includes a detailed review of gasification and plasma fundamentals in relation to the specific process, along with insight on MSW based feedstock properties and sulphur pollutant therein. As a first step to understanding sulphur partitioning and speciation within the process, thermodynamic modelling of the fluid bed stage has been performed. Preliminary findings, supported by plant experience, indicate the prominence of solid phase sulphur species (as opposed to H{sub 2}S) - Na and K based species in particular. Work is underway to further investigate and validate this.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2012

Keweenaw Bay Indian Community Weatherization Training Project Final Report

The objective of the project is to train the appropriate staff, both new and existing from various KBIC Departments in the core competencies needed to develop a comprehensive Tribal community weatherization program. The goal of the project was to build the staff capacity to enable the KBIC to establish a Tribal weatherization program that promotes energy sufficiency throughout the Tribal community. The project addresses the needs identified in the 2008 KBIC Strategic Energy Plan to build the staff capacity and increase the knowledge base within the Tribal Departments and to implement an energy efficiency program through community education and financial assistance incentives.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2014

Neutronics Analyses of the Minimum Original HEU TREAT Core

This work was performed to support the feasibility study on the potential conversion of the Transient Reactor Test Facility (TREAT) at Idaho National Laboratory from the use of high-enriched uranium (HEU) fuel to the use of low-enriched uranium (LEU) fuel. The analyses were performed by the GTRI Reactor Conversion staff at the Argonne National Laboratory (ANL). The objective of this study was to validate the MCNP model of the TREAT reactor with the well-documented measurements which were taken during the start-up and early operation of TREAT. Furthermore, the effect of carbon graphitization was also addressed. The graphitization level was assumed to be 100% (ANL/GTRI/TM-13/4). For this purpose, a set of experiments was chosen to validate the TREAT MCNP model, involving the approach to criticality procedure, in-core neutron flux measurements with foils, and isothermal temperature coefficient and temperature distribution measurements. The results of this study extended the knowledge base for the TREAT MCNP calculations and established the credibility of the MCNP model to be used in the core conversion feasibility analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2012

High Penetration of Renewable Energy in the Transportation Sector: Scenarios, Barriers, and Enablers; Preprint

Transportation accounts for 71% of U.S. petroleum use and 33% of its greenhouse gases emissions. Pathways toward reduced greenhouse gas emissions and petroleum dependence in the transportation sector have been analyzed in considerable detail, but with some limitations. To add to this knowledge, the U.S. Department of Energy has launched a study focused on underexplored greenhouse-gas-abatementand oil-savings opportunities related to transportation. This Transportation Energy Futures study analyzes specific issues and associated key questions to strengthen the existing knowledge base and help cultivate partnerships among federal agencies, state and local governments, and industry.

02 PETROLEUM↗

DOE OSTI2016

Regulatory Technology Development Plan - Sodium Fast Reactor: Mechanistic Source Term – Trial Calculation

The potential release of radioactive material during a plant incident, referred to as the source term, is a vital design metric and will be a major focus of advanced reactor licensing. The U.S. Nuclear Regulatory Commission has stated an expectation for advanced reactor vendors to present a mechanistic assessment of the potential source term in their license applications. The mechanistic source term presents an opportunity for vendors to realistically assess the radiological consequences of an incident, and may allow reduced emergency planning zones and smaller plant sites. However, the development of a mechanistic source term for advanced reactors is not without challenges, as there are often numerous phenomena impacting the transportation and retention of radionuclides. This project sought to evaluate U.S. capabilities regarding the mechanistic assessment of radionuclide release from core damage incidents at metal fueled, pool-type sodium fast reactors (SFRs). The purpose of the analysis was to identify, and prioritize, any gaps regarding computational tools or data necessary for the modeling of radionuclide transport and retention phenomena. To accomplish this task, a parallel-path analysis approach was utilized. One path, led by Argonne and Sandia National Laboratories, sought to perform a mechanistic source term assessment using available codes, data, and models, with the goal to identify gaps in the current knowledge base. The second path, performed by an independent contractor, performed sensitivity analyses to determine the importance of particular radionuclides and transport phenomena in regards to offsite consequences. The results of the two pathways were combined to prioritize gaps in current capabilities.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2019

Tank Side Cesium Removal System Project and Technology Maturation Program - 19519

Washington River Protection Solutions (WRPS) is the Tank Operating Contractor (TOC) for the U.S. Department of Energy-Office of River Protection (DOE-ORP) on the Hanford Site. The Hanford Site stores an estimated 56 million gallons of mixed radioactive and chemically hazardous waste in large underground tanks. WRPS is in the process of designing the Tank Side Cesium Removal (TSCR) system to produce Low Activity Waste (LAW) from Hanford tank waste. The LAW will be transferred to the Waste Treatment and Immobilization Plant (WTP) LAW Vitrification Facility, where the hazardous constituents in the LAW will be immobilized in a durable glass waste form for disposal. The TSCR Project demonstrates a tank-side treatment system for providing feed to the WTP LAW Vitrification Facility. This system removes cesium from supernatant tank waste using non-elutable cesium ion exchange media to meet the applicable waste acceptance criteria for the WTP. In support of the project, a tailored Technology Maturation/Testing Plan (TMP) has been developed to identify principal Technology Elements (TEs) for the proposed process, designate Critical Technology Elements (CTEs), identify gaps in the existing knowledge base and identify the necessary activities to mature the technologies for successful deployment of TSCR. The project scope and technology maturation approach are presented. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2014

SU-E-T-51: Bayesian Network Models for Radiotherapy Error Detection

Purpose: To develop a probabilistic model of radiotherapy plans using Bayesian networks that will detect potential errors in radiation delivery. Methods: Semi-structured interviews with medical physicists and other domain experts were employed to generate a set of layered nodes and arcs forming a Bayesian Network (BN) which encapsulates relevant radiotherapy concepts and their associated interdependencies. Concepts in the final network were limited to those whose parameters are represented in the institutional database at a level significant enough to develop mathematical distributions. The concept-relation knowledge base was constructed using the Web Ontology Language (OWL) and translated into Hugin Expert Bayes Network files via the the RHugin package in the R statistical programming language. A subset of de-identified data derived from a Mosaiq relational database representing 1937 unique prescription cases was processed and pre-screened for errors and then used by the Hugin implementation of the Estimation-Maximization (EM) algorithm for machine learning all parameter distributions. Individual networks were generated for each of several commonly treated anatomic regions identified by ICD-9 neoplasm categories including lung, brain, lymphoma, and female breast. Results: The resulting Bayesian networks represent a large part of the probabilistic knowledge inherent in treatment planning. By populating the networks entirely with data captured from a clinical oncology information management system over the course of several years of normal practice, we were able to create accurate probability tables with no additional time spent by experts or clinicians. These probabilistic descriptions of the treatment planning allow one to check if a treatment plan is within the normal scope of practice, given some initial set of clinical evidence and thereby detect for potential outliers to be flagged for further investigation. Conclusion: The networks developed here support the use of probabilistic models into clinical chart checking for improved detection of potential errors in RT plans.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

International Collaboration Activities in Different Geologic Disposal Environments

This report describes the current status of international collaboration regarding geologic disposal research in the Used Fuel Disposition (UFD) Campaign. Since 2012, in an effort coordinated by Lawrence Berkeley National Laboratory, UFD has advanced active collaboration with several international geologic disposal programs in Europe and Asia. Such collaboration allows the UFD Campaign to benefit from a deep knowledge base with regards to alternative repository environments developed over decades, and to utilize international investments in research facilities (such as underground research laboratories), saving millions of R&D dollars that have been and are being provided by other countries. To date, UFD’s International Disposal R&D Program has established formal collaboration agreements with five international initiatives and several international partners, and national lab scientists associated with UFD have conducted specific collaborative R&D activities that align well with its R&D priorities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗