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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Supporting information for Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

In this work, we use few-shot learning to segment the body and vein architecture of P. trichocarpa leaves from high-resolution scans obtained in the UC Davis common garden. Leaf and vein segmentation are formulated as separate tasks, in which convolutional neural networks (CNNs) are used to iteratively expand partial segmentations until reaching stopping criteria. Our leaf and vein segmentation approaches use just 50 and 8 manually traced images for training, respectively, and are applied to a set of 2,634 top and bottom leaf scans. We show that both methods achieve high segmentation accuracy, in some cases exceeding even human-level segmentation. The leaf and vein segmentations are subsequently used to extract 68 morphological traits using traditional open-source image processing tools, which are validated using real-world physical measurements. For a biological perspective, we perform a genome-wide association study using the vein density trait to discover novel genetic architectures associated with multiple physiological processes relating to leaf development and function. In addition to sharing all of the few-shot learning code (see https://github.com/jlager/few-shot-leaf-segmentation), we are releasing all images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes. The data folder includes all images, ground truth segmentations, predicted segmentations, and extracted leaf traits. All images encode the sample ID in the file name by indicating the treatment, block, row, position, and leaf side, respectively. For example, the file, C_1_1_2_bot.jpeg, indicates the control treatment, block 1, row 1, position 2, and the bottom side of the leaf. Tabulated results include position IDs as well as the corresponding genotype IDs. The images folder includes the 2,906 high-resolution leaf scans taken in the field. The leaf_masks folder includes 50 ground truth segmentations used for training the leaf tracing algorithm. The leaf_preds folder includes the 2,906 predicted segmentations from the leaf tracing algorithm. The vein_masks folder includes 8 ground truth segmentations used for training the vein growing algorithm. The vein_preds folder includes the 1,453 predicted segmentations from the vein growing algorithm. The vein_probs folder includes the 1,453 predicted probability maps from the vein growing algorithm before thresholding. The genomes folder includes the set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes with a README file detailing the steps taken. The results folder includes: (i) raw values of the 68 predicted leaf traits in digital_traits.tsv, (ii) manually measured values of petiole length and width in manual_traits.tsv, (iii) thin plate spline (TPS) adjusted values of the vein density trait in vein_density_tps_adj.tsv, (iv) best linear unbiased prediction (BLUP) adjusted values of the vein density trait in vein_density_blups.tsv, and (v) GWAS results for the vein density trait, including chromosome positions and corresponding P values, in gwas_results.csv.

09 BIOMASS FUELS↗
DOE OSTI2026

Validation of an Erythema-Weighted UV Model Using Broadband Solar Irradiance Measurements From Eleven U.S. Sites: Preprint

Erythema-weighted UV solar irradiance (UV-E) has a potential impact on human health if the recommended maximum exposure times are exceeded. In spite of this, it is not measured at most sites that measure Global Horizontal Irradiance (GHI). However, since UV-E is highly correlated with GHI and total ozone content it can be estimated from this information with sufficient accuracy to assisst in public health recommendations. The Power Model (PM) provides a simple method to estimate the erythema-weighted UV irradiance (UV-E) from measured GHI, total ozone column and air mass. In this work, the performance of the PM method is assessed using high-quality data from 11 sites in the continental U.S. (part of SURFRAD and SOLRAD networks) and total ozone estimates publicly available from the MERRA-2 re-analysis database. A three year period (2021- 2023) at 1-minute frequency is considered. The results (for time aggregations of 5 and 60 minutes) show a high Pearson's correlations (> 0.99), consistently positive mean bias deviations (below 13%) and dispersions in the 8-19% range at all sites. Relative values are expressed in terms of the corresponding measurement mean. The performance indicators remain consistent across time resolutions (5 or 60 minutes), suggesting that the model's performance is robust and not significantly affected by short-term variability (which is captured by GHI). Spatial patterns reveal higher biases and RMSD in northern and eastern locations. These values represent a significant improvement over widely used satellite-based global UV-E estimates and open the possibility of using the PM with satellite-based GHI estimates for operational UV-E mapping over the contiguous U.S. territory.

14 SOLAR ENERGY↗
DOE OSTI2015

In vivo and in vitro toxicological effects of titanium dioxide nanoparticles on small intestine

In European Union, titanium dioxide (TiO{sub 2}) as bulk material is a food additive (E171) and - as nanoparticle (NP) - is used as a white pigment in several products (e.g. food, cosmetics, drugs). E171 contains approximately 36% of particles less than 100 nm in at least one dimension and TiO{sub 2} NP exposure is estimated fairly below 2.5 mg/person/day. The gastrointestinal tract is a route of entry for NPs, thus representing a potential target of effects. In in vivo study, the effects of TiO{sub 2} NP in adult rat small intestine have been evaluated by oral administration of 0 (CTRL), 1 and 2 mg/kg body weight per day - relevant to human dietary intake. Detailed quali/quantitative histopathological analyses were performed on CTRL and treated rat samples. Scanning electron microscopy (SEM) analysis was performed on small intestine. An in vitro study on Caco-2 cells was also used in order to evaluate the potential cytotoxic effects directly on enterocytes through the lactate dehydrogenase (LDH) assay. Suspensions of TiO{sub 2} NPs for in vitro and in vivo study were characterized by EM. Histomorphometrical data showed treatment-related changes of villus height and widths in male rats. Significantly different from CTRL decreased LDH levels in the medium were detected in vitro at 24h with 2.5, 5, 10 and 20 µg/cm{sup 2} levels of TiO{sub 2} NPs. SEM analysis showed no damaged areas. Overall the results showed that enterocytes may represent a target of TiO{sub 2} NP toxicity by direct exposure both in vivo and in vitro models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Prevalence of human papillomavirus genotypes in high-grade cervical precancer and invasive cervical cancer from cancer registries before and after vaccine introduction in the United States

Background. US population-based cancer registries can be used for surveillance of human papillomavirus (HPV) types found in HPV-associated cancers. Using this framework, HPV prevalence among high-grade cervical precancers and invasive cervical cancers were compared before and after HPV vaccine availability. Methods. Archived tissue from 2 studies of cervical precancers and invasive cervical cancers diagnosed from 1993-2005 (prevaccine) were identified from 7 central cancer registries in Florida; Hawaii; Iowa; Kentucky; Louisiana; Los Angeles County, California; and Michigan; from 2014 through 2015 (postvaccine) cases were identified from 3 registries in Iowa, Kentucky, and Louisiana. HPV testing was performed using L1 consensus polymerase chain reaction analysis. HPV-type–specific prevalence was examined grouped by hierarchical attribution to vaccine types: HPV 16, 18, HPV 31, 33, 45, 52, 58, other oncogenic HPV types, and other types/HPV negative. Generalized logit models were used to compare HPV prevalence in the prevaccine study to the postvaccine study by patient age, adjusting for sampling factors. Results. A total of 676 precancers (328 prevaccine and 348 postvaccine) and 1140 invasive cervical cancers (777 prevaccine and 363 postvaccine) were typed. No differences were observed in HPV-type prevalence by patient age between the 2 studies among precancers or invasive cancers. Conclusions. The lack of reduction in vaccine-type prevalence between the 2 studies is likely explained by the low number of cases and low HPV vaccination coverage among women in the postvaccine study. Monitoring HPV-type prevalence through population-based strategies will continue to be important in evaluating the impact of the HPV vaccine.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Explainable Neural Architecture Search (XNAS)

Code for the paper Learning Interpretable Models Through Multi-Objective Neural Architecture Search by Zachariah Carmichael, Tim Moon, and Sam Ade Jacobs. Monumental advances in deep learning have led to unprecedented achievements across a multitude of domains. While the performance of deep neural networks is indubitable, the architectural design and interpretability of such models are nontrivial. Research has been introduced to automate the design of neural network architectures through neural architecture search (NAS). Recent progress has made these methods more pragmatic by exploiting distributed computation and novel optimization algorithms. However, there is little work in optimizing architectures for interpretability. To this end, we propose a multiobjective distributed NAS framework that optimizes for both task performance and introspection. We leverage the non-dominated sorting genetic algorithm (NSGA-II) and explainable AI (XAI) techniques to reward architectures that can be better comprehended by humans. The framework is evaluated on several image classification datasets. We demonstrate that jointly optimizing for introspection ability and task error leads to more disentangled architectures that perform within tolerable error.

Carmichael, ZachariahJ↗
DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Airport Ground Support Equipment Infrastructure & Logistics Electrification Assessment Tool: 2025 Data Development, Modeling and Analysis for DFW

The aviation industry is increasingly turning to modernize freight facilities by integrating electric Ground Support Equipment (eGSE) to enhance operational efficiency of freight facility moving vehicles and equipment. Airports worldwide are adopting eGSE to streamline cargo movement, reduce fuel and maintenance costs, and improve logistics coordination.1 North America, with its advanced aviation infrastructure, leads this transition, leveraging Internet of things (IoT)-enabled automation and zero emission technologies to boost reliability and reduce human errors.2 Electrification of freight facility moving vehicles and equipment boosts turnaround times, improves equipment reliability, and optimizes logistics coordination, giving operators a competitive advantage. With rising fuel price volatility and the pressure to meet stringent performance benchmarks, airports are focusing on cost-effective, scalable solutions for long-term financial and operational gains. To further accelerate electrification, airports are integrating Zero Emission Vehicles (ZEVs) into rental car fleets and deploying electric baggage carts, requiring strategic investments in charging infrastructure. 3 The shift, however, presents challenges, such as limited technical expertise, high capital costs, and complex procurement processes. By forging strategic partnerships, leveraging advanced technologies, and optimizing infrastructure investments, airports can create a resilient, future-ready ecosystem that enhances the movement of people and goods through electrification-driven efficiency. Supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO), this electrification effort provides a scalable, cost-effective solution to improve airport freight operations. Through targeted investments and innovation, airports enhance efficiency, reduce costs, and meet performance benchmarks while advancing toward a resilient, electrified future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

Varela, Sebastian↗
DOE OSTI2018

A New Black Carbon Sensor for Dense Air Quality Monitoring Networks

Low-cost air pollution sensors are emerging and increasingly being deployed in densely distributed wireless networks that provide more spatial resolution than is typical in traditional monitoring of ambient air quality. However, a low-cost option to measure black carbon (BC)—a major component of particulate matter pollution associated with adverse human health risks—is missing. This paper presents a new BC sensor designed to fill this gap, the Aerosol Black Carbon Detector (ABCD), which incorporates a compact weatherproof enclosure, solar-powered rechargeable battery, and cellular communication to enable long-term, remote operation. This paper also demonstrates a data processing methodology that reduces the ABCD’s sensitivity to ambient temperature fluctuations, and therefore improves measurement performance in unconditioned operating environments (e.g., outdoors). A fleet of over 100 ABCDs was operated outdoors in collocation with a commercial BC instrument (Magee Scientific, Model AE33) housed inside a regulatory air quality monitoring station. The measurement performance of the 105 ABCDs is comparable to the AE33. The fleet-average precision and accuracy, expressed in terms of mean absolute percentage error, are 9.2 ± 0.8% (relative to the fleet average data) and 24.6 ± 0.9% (relative to the AE33 data), respectively (fleet-average ± 90% confidence interval).

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Jiangsu Nanjing – Lawrence Berkeley Life Sciences Center for Translational Medicine (CRADA AWD00000392)

Project 1: Our previous work demonstrated that the CES biomarker can be an approximate measure for the levels of chromosomal instability (CIN) in cancer, and that the CES (for Centromere and kinetochore gene Expression Score) biomarker effectively predicts which cancer patients benefit from adjuvant chemotherapies in lung and other cancer types. Such a utility can potentially reduce overtreatment that is common in lung cancer treatment. We determined performance of the multiplex Quantigene RNA assay platform for measuring mRNA levels of CES genes using clinical tumor samples. These experiments determined sensitivity, specificity and detection limit of 12 CES genes and 5 control genes using clinical tumor samples with paired normal tissue samples. We determined how sample concentration and storage conditions affect measurement sensitivity and specificity, and whether sample storage over time affects result reproducibility. Importantly, we confirmed that early stage lung cancer CES values are significantly higher than those of paired normal tissues in all cases. This result greatly extended our bioinformatic data mining by confirming that we can measure CES values directly from patient tumor tissue, and that tumors have higher CES values than normal tissues. Unfortunately, our CRADA partner in China did not finish measuring CES expression levels for a larger group of ~100 tumor samples, due to the long period time required for human subject review in China. This delay prevented us from generating enough additional data to generate a significant statistical model. We finished analyzing all the data acquired during the CRADA, and are trying to work collaboratively with the Shanghai University Lung Cancer Hospital to complete the analysis of more tumor samples. Although no publications have resulted from this research, the findings are crucial for ongoing efforts to obtain funding to develop the technology to address problems in deciding on appropriate treatment regimens for individual patients, in clinical settings. Project 2: The overarching milestones of CRADA Project 2 were to investigate the role of FAM83 family members in cancer progression. Two publications resulted from this work which are highlighted below. The first major goal of the CRADA was to broadly assess the role of FAM83 family oncogenes in human cancer using a multi-omic bioinformatic approach (Snijders et al 2017 Mol. Oncol 11). The second major goal was to specifically assess the role of FAM83D in a cancer progression and metastasis (Yin et al 2019 Cell Oncol; in revision). Role of FAM83 family oncogenes in human cancers Recent studies from our laboratory as well as other groups have demonstrated that FAM83 family members have oncogenic properties and have significantly elevated expression levels in multiple human tumor types, including breast cancers (BC). We performed an integrative multi-omics bio-informatic analysis of the role of FAM83 family oncogenes in human cancers. Our findings were published in the journal Molecular Oncology (Snijders et al; Mol Oncol. 2017 Feb;11(2):167-179). Highlights from this manuscript are: • Transcriptional levels of FAM83 family members are frequently and dramatically elevated across tumor types in human • DNA copy number increase appears to be a potential mechanism for increased expression of FAM83 genes • FAM83 family gene signature is associated with poor survival across human tumor types • FAM83 gene alterations are significantly correlated with TP53, PIK3CA and Ecadherin mutations. In conclusion, this analysis of FAM83 family members opened up a new horizon for further understanding the significance of FAM83 genes in cancer and clinical applications in diagnosis, prognosis and therapy. Role of FAM83D in human non-small cell lung cancer (NSCLC) metastasis and its impact on chemotherapy. Lung cancer is one of the most common malignant tumors in the world and non-small cell lung cancer (NSCLC) accounts for 80~85% of all diagnosed lung cancer cases, one of the deadliest cancers worldwide. FAM83D has been proposed as an oncoprotein in several types of human cancer, yet its role and molecular mechanism in human non-small cell lung cancer (NSCLC) metastasis and its impact on chemotherapy are poorly understood. We evaluated the expression of FAM83D in NSCLC cells and cancerous tissues and its prognostic significance, then investigated the effect of FAM83D on tumorigenic phenotypes such as cell proliferation, EMT and invasive capability, and finally assessed impact of FAM83D on NSCLC chemo-drug resistance. The results of this study are currently in revision at the journal Cellular Oncology. Highlights from this manuscript are: • FAM83D expression is elevated in NSCLC cells and tissues and associated with poor prognosis • FAM83D promotes NSCLC cell proliferation • FAM83D induces EMT in NSCLC cells • FAM83D promotes NSCLC cell migration and invasion • FAM83D promotes EMT and invasive capability of NSCLC cells by regulating AKT/mTOR pathway • FAM83D silencing increases cisplatin sensitivity of NSCLC cells In summary, this second study demonstrated for the first time that FAM83D promoted EMT and metastasis of NSCLC cells through AKT/mTOR signal pathway as well as increased the sensitivity of NSCLC to cisplatin. This study may provide potential targets for advanced NSCLC treatment and personalized therapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

The Strategic Value of Human Factors Engineering in Control Room Modernization

Control room modernization is one of the most challenging and complex upgrade projects that a nuclear power plant can undertake. It can have almost as big an impact on operations as, for example, turbine replacement. The challenges of migrating an analog control system to a distributed control system are already well known and a number of nuclear utilities have embarked upon various levels of effort to upgrade some of the systems in the control room. When planning for control room upgrades, plants have to deal with a multitude of engineering, operational, and regulatory impacts. This will inevitably include several human factors considerations, such as workstation ergonomics, viewing angles, lighting, seating, new interaction modalities, new communication requirements, and new concepts of operation. In helping nuclear power utilities to deal with these challenges, the United States Department of Energy researchers located at Idaho National Laboratory (INL) have developed research-based human factors design and evaluation methods to be used in the development of end-state concepts for modernized control rooms and to manage the various phases of the upgrade life cycle. The methodology includes interactive sessions with operators in INL’s Human System Simulation Laboratory, three-dimensional modeling to visualize control board changes and operator-system interaction, and development of human-system prototypes to evaluate various aspects of proposed modifications. This methodology has been applied at a number of U.S. nuclear power plants where modernization projects are underway, including Exelon’s Braidwood and Byron plants, and Arizona Public Service's Palo Verde plant. It was demonstrated that including this methodology in the plant’s engineering process helps to ensure an integrated and cohesive outcome that is consistent with human factors engineering principles and provide substantial improvement in operator performance.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

The Center for Performance and Design of Nuclear Waste Forms and Containers (WastePD) Energy Frontier Research Center (Final Report)

The DOE Office of Environmental Management is responsible for high level nuclear waste that must be safely isolated from humans and the environment for extremely long periods. The waste forms and containers are made of glass, ceramics, and metals. Verified safe disposal requires understanding the fundamental mechanisms of waste form degradation and the design of new waste forms with improved performance, which comprise the goals of the Energy Frontier Research Center known as the Center for the Performance and Design of Nuclear Waste Forms and Containers, WastePD. WastePD was constructed to develop innovative approaches and solutions to those goals through the synergistic interactions of individuals who are experts in the degradation behavior, modeling, and design of glasses, ceramics and metal alloys. WastePD is the first center ever created to address this diverse group of materials in a comprehensive and coordinated manner. The science goals are grouped into three common topics: corrosion mechanisms via advanced characterization, environmental impacts, and materials design. Synergistic interactions in these areas were a key component of WastePD. The fundamental understanding of the degradation mechanisms of the waste forms and containers as well as the development of new materials with improved properties will allow DOE to prevent environmental contamination and to explore totally new repository concepts. WastePD was operational from August 2016 through July 2022, but the DOE support was drastically reduced for the last two years. This final technical report covers the full period of performance. However, much of what was accomplished in the first four years is nicely summarized in a review paper published in 2021, which is appended to this report. Therefore, this final report focuses on the technical findings from the last two years of WastePD activities. Considerable progress was made in the areas of a) the environmental and compositional impacts on the corrosion of borosilicate and aluminosilicate glasses, b) the mechanism of glass corrosion and the structure and evolution of the surface alteration layer, c) the effects of environment and composition on the corrosion of pyrochlore, perovskite, hollandite, and other oxide ceramics, d) the corrosion mechanism of multi-principal element metallic alloys, and e) a new framework for understanding the pitting corrosion of metals.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Towards Agentic AI on Particle Accelerators

As particle accelerators grow in complexity, traditional control methods face increasing challenges in achieving optimal performance. This paper envisions a paradigm shift: a decentralized multi-agent framework for accelerator control, powered by Large Language Models (LLMs) and distributed among autonomous agents. We present a proposition of a self-improving decentralized system where intelligent agents handle high-level tasks and communication and each agent is specialized control individual accelerator components. This approach raises some questions: What are the future applications of AI in particle accelerators? How can we implement an autonomous complex system such as a particle accelerator where agents gradually improve through experience and human feedback? What are the implications of integrating a human-in-the-loop component for labeling operational data and providing expert guidance? We show two examples, where we demonstrate viability of such architecture.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

Human machine interface to manually drive rhombic like vehicles such as transport casks in ITER

The Cask and Plug Remote Handling System (CPRHS) and the respective Cask Transfer System (CTS) are designed to transport activated components between the reactor and the hot cell buildings of ITER during maintenance operations. In nominal operation, the CPRHS/CTS shall operate autonomously under human supervision. However, in some unexpected situations, the automatic mode must be overridden and the vehicle must be remotely guided by a human operator due to the harsh conditions of the environment. The CPRHS/CTS is a rhombic-like vehicle with two independent steerable and drivable wheels along its longitudinal axis, giving it omni-directional capabilities. During manual guidance, the human operator has to deal with four degrees of freedom, namely the orientations and speeds of two wheels. This work proposes a Human Machine Interface (HMI) to manage the degrees of freedom and to remotely guide the CPRHS/CTS in ITER taking the most advantages of rhombic like capabilities. Previous work was done to drive each wheel independently, i.e., control the orientation and speed of each wheel independently. The results have shown that the proposed solution is inefficient. The attention of the human operator becomes focused in a single wheel. In addition, the proposed solution cannot assure that the commands accomplish the physical constrains of the vehicle, resulting in slippage or even in clashes. This work proposes a solution that consists in the control of the vehicle looking at the position of its center of mass and its heading in the world frame. The solution is implemented using a rotational disk to control the vehicle heading and a common analogue joystick to control the vector speed of the center of the mass of the vehicle. The number of degrees of freedom reduces to three, i.e., two angles (vehicle heading and the orientation of the vector speed) and a scalar (the magnitude of the speed vector). This is possible using a kinematic model based on the vehicle Instantaneous Center of Rotation (ICR): a geometric approach where, at each time instant, the vehicle describes a circumference (either with a finite or infinite radius). The inverse of the kinematic model transforms the three input parameters of the center of mass into the four parameters for the wheels, preserving the omni-directional capabilities. The solution is implemented and tested using a HMI with a control disk and an analog joystick with two axis. The control disk was specially designed for this solution and implemented using a programmable micro-controller. In the first set of experiments, the HMI communicates with a computer running a simulator of the CPRHS/CTS, with the vehicle kinematics and dynamics, moving in a map of the ITER buildings. In the second set of experiments, the HMI communicates with a scaled prototype of the CPRHS running in a mock-up scenario to obtain more realistic results. Several type of tests were performed to evaluate the usability of the HMI. Different human operators without knowledge neither experience with this interface were invited to test the HMI. The operators had to drive the vehicle from an initial place to a final destination under the following conditions: with a pre-computed path to help guidance, without any path, with the information of the closest obstacles and without any help. The performance was evaluated using the time duration of the operation, the energy required to perform the described path, the risk of collision and, in case of a pre-computed path, the comparison between paths. In addition, each operator tested the HMI several times to evaluate the performance along consecutive trials. (authors)

42 ENGINEERING↗
DOE OSTI2017

Differential gene expression profiling of human adipose stem cells differentiating into smooth muscle-like cells by TGFβ1/BMP4

Regenerative repair of the vascular system is challenging from the perspectives of translational medicine and tissue engineering. There are fundamental hurdles in front of creating bioartificial arteries, which involve recaputilation of the three-layered structure under laboratory settings. Obtaining and maintaining smooth muscle characteristics is an important limitation, as the transdifferentiated cells fail to display mature phenotype. This study aims to shed light on the smooth muscle differentiation of human adipose stem cells (hASCs). To this end, we first acquired hASCs from lipoaspirate samples. Upon characterization, the cells were induced to differentiate into smooth muscle (SM)-like cells using a variety of inducer combinations. Among all, TGFβ1/BMP4 combination had the highest differentiation efficiency, based on immunohistochemical analyses. hSM-like cell samples were compared to hASCs and to the positive control, human coronary artery-smooth muscle cells (hCA-SMCs) through gene transcription profiling. Microarray findings revealed the activation of gene groups that function in smooth muscle differentiation, signaling pathways, extracellular modeling and cell proliferation. Our results underline the effectiveness of the growth factors and suggest some potential variables for detecting the SM-like cell characteristics. Evidence in transcriptome level was used to evaluate the TGFβ1/BMP4 combination as a previously unexplored effector for the smooth muscle differentiation of adipose stem cells. - Highlights: • Human adipose stem cells (hASCs) were isolated, characterized and cultured. • Growth factor combinations were evaluated for their effectiveness in differentiation using IHC. • hASCs were differentiated into smooth muscle (SM)-like cells using TGF-β1 and BMP4 combination. • Microarray analysis was performed for hASCs, SM-like cells and coronary artery-SMCs. • Microarray data was used to perform hierarchical clustering and interpretation of activated pathways.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

An Enzyme-Free Signal Amplification Technique for Ultrasensitive Colorimetric Assay of Disease Biomarkers

Enzyme-based colorimetric assays have been widely used in research labs and clinical diagnosis for decades. Nevertheless, as constrained by the performance of enzymes, their detection sensitivity has not been substantially improved in recent years, which inhibits many critical applications such as early detection of cancers. In this work, we demonstrate an enzyme-free signal amplification technique, based on gold vesicles encapsulated with Pd-Ir nanoparticles as peroxidase mimics, for colorimetric assay of disease biomarkers with significantly enhanced sensitivity. This technique overcomes the intrinsic limitations of enzymes, thanks to the superior catalytic efficiency of peroxidase mimics and the efficient loading and release of these mimics. Using human prostate surface antigen as a model biomarker, we demonstrated that the enzyme-free assay could reach a limit of detection at the femtogram/mL level, which is over 10 3 -fold lower than that of conventional enzyme-based assay when the same antibodies and similar procedure were used.

59 BASIC BIOLOGICAL SCIENCES↗