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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mechanical separations of corn stover anatomical fractions in an integrated feedstock preprocessing system: An experimental and data-driven modeling study

High variabilities of material attributes in lignocellulosic biomass present risks for biofuel and biochemical productions and must be mitigated via preprocessing. Since almost no mechanical device is originally designed for processing biomass, how to operate existing apparatuses with efficient performance has not been investigated extensively. This work presents a study on an integrated screening and air classification to separate cobs and stalks from husks and leaves in corn stover. Prototype machine learning models were developed to assess the feasibility of predicting the process outcome based on the measurable parameters. The models trained upon limited experimental data rendered decent predictive accuracy of yield and purity. The experimental data and modeling results collectively suggest decreasing throughput leads to a higher purity. To the contrary, if throughput increases, a lower purity is likely. A possible trade-off between yield and purity of the separated streams indicates the need for optimal combinations of feedstock size, moisture, and throughput to achieve optimized separations. The results of this study also suggest the need to further improve model predictability by developing more accurate formulations for physics governing the integrated unit operations. To accomplish this, additional experimental data needs to be generated for model training.

09 - BIOMASS FUELS↗
DOE OSTI2025

Novel CHI3L1 ‐Associated Angiogenic Phenotypes Define Glioma Microenvironments: Insights From Multi‐Omics Integration

ABSTRACT The CHI3L1 signaling pathway significantly influences glioma angiogenesis, but its role in the tumor microenvironment (TME) remains elusive. We propose a novelCHI3L1‐associated vascular phenotype classification for glioma through integrative analyses of multiple datasets with bulk and single‐cell transcriptome, genomics, digital pathology, and clinical data. We investigated the biological characteristics, genomic alterations, therapeutic vulnerabilities, and immune profiles within these phenotypes through a comprehensive multi‐omics approach. We constructed the vascular‐related risk (VR) score based onCHI3L1‐associated vascular signatures (CAVS) identified by machine learning algorithms. Utilizing unsupervised consensus clustering, gliomas were stratified into three distinct vascular phenotypes: Cluster A, marked by high vascularization and stromal activation with a relatively low levels of tumor‐infiltrating lymphocytes (TILs); Cluster B, characterized by moderate vascularization and stromal activity, coupled with a high density of TILs; and Cluster C, defined by low vascularization and sparse immune cell infiltration. We observed that the CAVS effectively indicated glioma‐associated angiogenesis and immune suppression by single‐cell RNA‐seq analysis. Moreover, the high‐VR‐score group exhibited enhanced angiogenic activity, reduced immune response, resistance to immunotherapy, and poorer clinical outcomes. The VR score independently predicted glioma prognosis and, combined with a nomogram, provided a robust clinical decision‐making tool. Potential drug prediction based on transcription factors for high‐risk patients was also performed. Our study reveals thatCHI3L1‐associated vascular phenotypes shape distinct immune landscapes in gliomas, offering insights for optimizing therapeutic strategies to improve patient outcomes.

Oncology↗
DOE OSTI2014

Rare key functional domain missense substitutions in MRE11A, RAD50 , and NBN contribute to breast cancer susceptibility: results from a Breast Cancer Family Registry case-control mutation-screening study

The MRE11A-RAD50-Nibrin (MRN) complex plays several critical roles related to repair of DNA double-strand breaks. Inherited mutations in the three components predispose to genetic instability disorders and the MRN genes have been implicated in breast cancer susceptibility, but the underlying data are not entirely convincing. Here, we address two related questions: (1) are some rare MRN variants intermediate-risk breast cancer susceptibility alleles, and if so (2) do the MRN genes follow a BRCA1/BRCA2 pattern wherein most susceptibility alleles are protein-truncating variants, or do they follow an ATM/CHEK2 pattern wherein half or more of the susceptibility alleles are missense substitutions? Using high-resolution melt curve analysis followed by Sanger sequencing, we mutation screened the coding exons and proximal splice junction regions of the MRN genes in 1,313 early-onset breast cancer cases and 1,123 population controls. Rare variants in the three genes were pooled using bioinformatics methods similar to those previously applied to ATM, BRCA1, BRCA2, and CHEK2, and then assessed by logistic regression. Results: Re-analysis of our ATM, BRCA1, and BRCA2 mutation screening data revealed that these genes do not harbor pathogenic alleles (other than modest-risk SNPs) with minor allele frequencies >0.1% in Caucasian Americans, African Americans, or East Asians. Limiting our MRN analyses to variants with allele frequencies of <0.1% and combining protein-truncating variants, likely spliceogenic variants, and key functional domain rare missense substitutions, we found significant evidence that the MRN genes are indeed intermediate-risk breast cancer susceptibility genes (odds ratio (OR) = 2.88, P = 0.0090). Key domain missense substitutions were more frequent than the truncating variants (24 versus 12 observations) and conferred a slightly higher OR (3.07 versus 2.61) with a lower P value (0.029 versus 0.14). These data establish that MRE11A, RAD50, and NBN are intermediate-risk breast cancer susceptibility genes. Like ATM and CHEK2, their spectrum of pathogenic variants includes a relatively high proportion of missense substitutions. However, the data neither establish whether variants in each of the three genes are best evaluated under the same analysis model nor achieve clinically actionable classification of individual variants observed in this study.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Image Classification Using Convolutional Neural Networks to Automate Visual Inspection of CCO Containers (Rev. 1)

The Savannah River Site is automating the receipt and inspection of Criticality Control Overpack drums to aid in nuclear waste disposal. The Advanced Engineering group is automating this receipt and inspection process to assist the site’s goals. Part of this process includes visually inspecting CCO drums to ensure that no defects or security risks are present. This research focuses on developing a machine learning model to automatically classify drums as passing or failing inspection. The machine learning model was implemented using the open-source Tensorflow library and uses a Convolutional Neural Network architecture to extract features and differentiate between images. The model was able to achieve an average of 85% accuracy on a dataset of 288 drums. This project’s goal was to prove the validity of computer vision in an automation process and provide a starting place for continued research.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2010

Human Factors Research for Space Exploration: Measurement, Modeling, and Mitigation

As part of NASA's Human Research Program, the Space Human Factors Engineering Project serves as the bridge between Human Factors research and Human Spaceflight applications. Our goal is to be responsive to the operational community while addressing issues at a sufficient level of abstraction to ensure that our tools and solutions generalize beyond the point design. In this panel, representatives from four of our research domains will discuss the challenges they face in solving current problems while also enabling future capabilities. Historically, engineering-dominated organizations have tended to view good Human Factors (HF) as a desire rather than a requirement in system design and development. Our field has made significant gains in the past decade, however; the Department of Defense, for example, now recognizes Human-System Integration (HSI), of which HF is a component, as an integral part of their divisions hardware acquisition processes. And our own agency was far more accepting of HF/HSI requirements during the most recent vehicle systems definition than in any prior cycle. Nonetheless, HF subject matter experts at NASA often find themselves in catch up mode... coping with legacy systems (hardware and software) and procedures that were designed with little regard for the human element, and too often with an attitude of we can deal with any operator issues during training. Our challenge, then, is to segregate the true knowledge gaps in Space Human Factors from the prior failures to incorporate best (or even good) HF design principles. Further, we strive to extract the overarching core HF issues from the point-design-specific concerns that capture the operators (and managers) attention. Generally, our approach embraces a 3M approach to Human Factors: Measurement, Modeling, and Mitigation. Our first step is to measure human performance, to move from subjective anecdotes to objective, quantified data. Next we model the phenomenon, using appropriate methods in our field, modifying them to suit the unique aspects of the space environment. Finally, we develop technologies, tools, and procedures to mitigate the decrements in human performance and capabilities that occur in space environments. When successful, we decrease risks to crew safety and to mission success. When extremely successful (or lucky), we devise generalizable solutions that advance the state of our practice. Our panel is composed of researchers from diverse domains of our project... from different boxes, if you will, of the Human Factors Analysis and Classification System (HFACS).

Kaiser, Mary K.↗
NASA NTRS2016

Example of Occupational Surveillance in a Telemedicine Setting: Application of Epidemiologic Methods at NASA Johnson Space Center

In 2010, NASA implemented Lifetime Surveillance of Astronaut Health, a formal occupational surveillance program for the U.S. astronaut corps. Because of the nature of the space environment, space medicine presents unique challenges and opportunities for epidemiologists. One such example is the use of telemedicine while crewmembers are in flight, where the primary source of information about crew health is verbal communication between physicians and their crewmembers. Due to restricted medical capabilities, the available health information is primarily crewmember report of signs and symptoms, rather than diagnoses. As epidemiologists at NASA, Johnson Space Center, we have shifted our paradigm from tracking diagnoses based on traditional terrestrial clinical practice to one in which we also incorporate reported symptomology as potential antecedents of disease. In this presentation we describe how characterization of reported signs and symptoms can be used to establish incidence rates for inflight immunologic events. We describe interdisciplinary data sources of information that are used in combination with medical information to analyze the data. We also delineate criteria for symptom classification inclusion. Finally, we present incidence tables and graphs to illustrate the final outcomes. Using signs and symptoms reported via telemedicine, the epidemiologists provide summary evidence regarding incidence of potential inflight medical conditions. These results inform our NASA physicians and scientists, and support evaluation of the occupational health risks associated with spaceflight.

Babiak-Vazquez, Adriana↗
DOE OSTI2016

Using individual patient anatomy to predict protocol compliance for prostate intensity-modulated radiotherapy

If a prostate intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT) plan has protocol violations, it is often a challenge knowing whether this is due to unfavorable anatomy or suboptimal planning. This study aimed to create a model to predict protocol violations based on patient anatomical variables and their potential relationship to target and organ at risk (OAR) end points in the setting of definitive, dose-escalated IMRT/VMAT prostate planning. Radiotherapy plans from 200 consecutive patients treated with definitive radiation for prostate cancer using IMRT or VMAT were analyzed. The first 100 patient plans (hypothesis-generating cohort) were examined to identify anatomical variables that predict for dosimetric outcome, in particular OAR end points. Variables that scored significance were further assessed for their ability to predict protocol violations using a Classification and Regression Tree (CART) analysis. These results were then validated in a second group of 100 patients (validation cohort). In the initial analysis of the hypothesis-generating cohort, percentage of rectum overlap in the planning target volume (PTV) (%OR) and percentage of bladder overlap in the PTV (%OB) were highlighted as significant predictors of rectal and bladder dosimetry. Lymph node treatment was also significant for bladder outcomes. For the validation cohort, CART analysis showed that %OR of < 6%, 6% to 9% and > 9% predicted a 13%, 63%, and 100% rate of rectal protocol violations respectively. For the bladder, %OB of < 9% vs > 9% is associated with 13% vs 88% rate of bladder constraint violations when lymph nodes were not treated. If nodal irradiation was delivered, plans with a %OB of < 9% had a 59% risk of violations. Percentage of rectum and bladder within the PTV can be used to identify individual plan potential to achieve dose-volume histogram (DVH) constraints. A model based on these factors could be used to reduce planning time, improve work flow, and strengthen plan quality and consistency.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Terrestrial acidification and ecosystem services: effects of acid rain on bunnies, baseball, and Christmas trees

Often termed “acid rain,” combined nitrogen and sulfur deposition can directly and indirectly impact the condition and health of forest ecosystems. Researchers use critical loads (CLs) to describe response thresholds, and recent studies on acid-sensitive biological indicators show that forests continue to be at risk from terrestrial acidification. However, rarely are impacts translated into changes in “ecosystem services” that impact human well-being. Further, the relevance of this research to the general public is seldom communicated in terms that can motivate action to protect valuable resources. To understand how changes in biological indicators affect human well-being, we used the STEPS (Stressor–Ecological Production function–final ecosystem Services) Framework to quantitatively and qualitatively link CL exceedances to ecosystem service impacts. We specified the cause-and-effect ecological processes linking changes in biological indicators to final ecosystem services. The Final Ecosystem Goods and Services Classification System (FEGS-CS) was used within the STEPS Framework to classify the ecosystem component and the beneficiary class that uses or values the component. We analyzed two acid-sensitive tree species, balsam fir (Abies balsamea) and white ash (Fraxinus americana), that are common in northeastern USA. These well-known species provide habitat for animals and popular forest products that are relatable to a broad audience. We identified 160 chains with 10 classes of human beneficiaries for balsam fir and white ash combined, concluding that there are resources at risk that the public may value. Two stories resulting from these explorations into the cascading effects of acid rain on terrestrial resources are ideal for effective science communication: the relationship between (1) balsam fir as a popular Christmas tree and habitat for the snowshoe hare, a favorite of wildlife viewers, and (2) white ash because it is used for half of all baseball bats, fine wood products, and musical instruments. Thus, rather than focusing on biological indicators that may only be understood or appreciated by specific stakeholders or experts, this approach extends the analysis to include impacts on FEGS and humans. It also lays the foundation for developing stakeholder-specific narratives, quantitative measures of endpoints, and for conducting demand-based valuations of affected ecosystem services.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Risk assessment of wellbore leakage during underground hydrogen storage

The expansion of renewable energy sources would require large-scale energy storage options to overcome the intermittent nature of these sources. Underground hydrogen storage (UHS) in depleted hydrocarbon reservoirs offers a scalable and practical energy storage solution. These reservoirs are chosen for their availability and large capacity, but the unique properties of hydrogen raise concerns about potential leakage pathways, particularly through wellbores. In this study, we develop and apply, for the first time, reduced-order models (ROMs) specifically designed for efficient leakage risk prediction in UHS systems operating in depleted hydrocarbon reservoirs. Using 3,000 high-fidelity simulation scenarios, we examine the influence of 11 key parameters, including reservoir and aquifer depths, wellbore permeability and porosity, initial saturations of water, oil and gas fractions (hydrogen, light, intermediate, and heavy hydrocarbons), reservoir pressure multiplier, and the aquifer-to-reservoir volume ratio, to simulate leakage behavior over a 1,000-year timescale. We train ROMs using a two-step classification-regression approach, achieving R 2 values exceeding 99 % across all targets. These ROMs effectively capture the leakage evolution and identify critical controls of leakage, guiding the design of mitigation strategies. Results indicate that gas leakage occurs in about 27 % of scenarios as early as five years post-operation, reaching volumes of up to 106 ft3. Oil leakage is less frequent (~17 %) and typically begins decades later. Our findings also show that hydrogen often migrates first, owing to its smaller molecular size and higher buoyancy, followed by heavier hydrocarbons. Over time, these heavier components contribute significantly to the total leaked volume, reinforcing the need for targeted monitoring and remediation strategies. Our analysis highlights that deeper storage reservoirs, shallower aquifers, and low-permeability wellbores significantly reduce leakage risks. In conclusion, this work offers a robust framework for risk-informed UHS deployment, supporting energy security through reliable large-scale hydrogen storage while safeguarding environmental integrity.

08 HYDROGEN↗
DOE OSTI2022

Autonomous System Subversion Tactics: Prototypes and Recommended Countermeasures

One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.

42 ENGINEERING↗
NASA NTRS2018

Assessing Global Surface Water Inundation Dynamics Using Combined Satellite Information from SMAP, AMSR2 and Landsat

A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fw(sub LBand)) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency (Tb) observations from AMSR2. The resulting (fw(sub LBand)) global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The (fw(sub LBand)) annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly (fw(sub LBand)) averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable (fw(sub LBand)) performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) (fw(sub LBand)) results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m (fw(sub LBand)) retrievals showed favourable spatial accuracy for water (70.71%) and land (98.99%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new (fw(sub LBand)) algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk.

WOD↗
DOE OSTI2015

Influence of source batch S{sub K} dispersion on dosimetry for prostate cancer treatment with permanent implants

Purpose: In clinical practice, specific air kerma strength (S{sub K}) value is used in treatment planning system (TPS) permanent brachytherapy implant calculations with {sup 125}I and {sup 103}Pd sources; in fact, commercial TPS provide only one S{sub K} input value for all implanted sources and the certified shipment average is typically used. However, the value for S{sub K} is dispersed: this dispersion is not only due to the manufacturing process and variation between different source batches but also due to the classification of sources into different classes according to their S{sub K} values. The purpose of this work is to examine the impact of S{sub K} dispersion on typical implant parameters that are used to evaluate the dose volume histogram (DVH) for both planning target volume (PTV) and organs at risk (OARs). Methods: The authors have developed a new algorithm to compute dose distributions with different S{sub K} values for each source. Three different prostate volumes (20, 30, and 40 cm{sup 3}) were considered and two typical commercial sources of different radionuclides were used. Using a conventional TPS, clinically accepted calculations were made for {sup 125}I sources; for the palladium, typical implants were simulated. To assess the many different possible S{sub K} values for each source belonging to a class, the authors assigned an S{sub K} value to each source in a randomized process 1000 times for each source and volume. All the dose distributions generated for each set of simulations were assessed through the DVH distributions comparing with dose distributions obtained using a uniform S{sub K} value for all the implanted sources. The authors analyzed several dose coverage (V{sub 100} and D{sub 90}) and overdosage parameters for prostate and PTV and also the limiting and overdosage parameters for OARs, urethra and rectum. Results: The parameters analyzed followed a Gaussian distribution for the entire set of computed dosimetries. PTV and prostate V{sub 100} and D{sub 90} variations ranged between 0.2% and 1.78% for both sources. Variations for the overdosage parameters V{sub 150} and V{sub 200} compared to dose coverage parameters were observed and, in general, variations were larger for parameters related to {sup 125}I sources than {sup 103}Pd sources. For OAR dosimetry, variations with respect to the reference D{sub 0.1cm{sup 3}} were observed for rectum values, ranging from 2% to 3%, compared with urethra values, which ranged from 1% to 2%. Conclusions: Dose coverage for prostate and PTV was practically unaffected by S{sub K} dispersion, as was the maximum dose deposited in the urethra due to the implant technique geometry. However, the authors observed larger variations for the PTV V{sub 150}, rectum V{sub 100}, and rectum D{sub 0.1cm{sup 3}} values. The variations in rectum parameters were caused by the specific location of sources with S{sub K} value that differed from the average in the vicinity. Finally, on comparing the two sources, variations were larger for {sup 125}I than for {sup 103}Pd. This is because for {sup 103}Pd, a greater number of sources were used to obtain a valid dose distribution than for {sup 125}I, resulting in a lower variation for each S{sub K} value for each source (because the variations become averaged out statistically speaking)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

On Finding Black Holes in Photometric Microlensing Surveys

There are expected to be millions of isolated black holes in the galaxy resulting from the deaths of massive stars. Measuring the abundance and properties of this remnant population would shed light on the end stages of stellar evolution and the evolution paths of black hole systems. Detecting isolated black holes is currently only possible via gravitational microlensing, which has so far yielded one definitive detection. The difficulty in finding microlensing black holes lies in having to choose a small subset of events, based on characteristics of their light curves, to allocate expensive and scarce follow-up resources to confirm the identity of the lens. Current methods either rely on simple cuts in parameter space without using the full distribution information or are only effective on small subsets of events. In this paper, we present a new lens classification method. The classifier takes in posterior constraints on light-curve parameters and combines them with a Galactic simulation to estimate the lens class probability. This method is flexible and can be used with any set of microlensing light-curve parameters, making it applicable to large samples of events. We make this classification framework available via the popclass Python package. We apply the classifier to ~10,000 microlensing events from the Optical Gravitational Lensing Experiment survey and find 23 high-probability black hole candidates. Our classifier also suggests that the only known isolated black hole is an observational outlier, according to current Galactic models, and the allocation of astrometric follow-up on this event was a high-risk strategy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions

The paper presents a novel approach to applying Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification tool to discern between a feasible and infeasible region. Bayesian optimization is a low-cost black-box global optimization tool in the Sequential Design Methods where one learns and updates knowledge from prior evaluated designs, and proceeds to the selection of new designs for future evaluation. However, BO is best suited to problems with the assumption of a continuous objective function and does not guarantee true convergence when having a discontinuous design space. This is because of the insufficient knowledge of the BO about the nature of the discontinuity of the unknown true function. In this paper, we have proposed to predict the location of the discontinuity using a BO algorithm on an artificially projected continuous design space from the original discontinuous design space. The proposed approach has been implemented in a thin tube design with the risk of creep-fatigue failure under constant loading of temperature and pressure. The stated risk depends on the location of the designs in terms of safe and unsafe regions, where the discontinuities lie at the transition between those regions; therefore, the discontinuity has also been treated as an unknown creep-fatigue failure constraint. The proposed BO algorithm has been trained to maximize sampling toward the unknown transition region, to act as a high accuracy classifier between safe and unsafe designs with minimal training cost. The converged solution has been validated for different design parameters with classification error rate and function evaluations at an average of <1% and ~150, respectively. Finally, the performance of our proposed approach in terms of training cost and classification accuracy of thin tube design is shown to be better than the existing machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Boosting.

Engineering↗
DOE OSTI2025

Functional protein mining with conformal guarantees

Molecular structure prediction and homology detection offer promising paths to discovering protein function and evolutionary relationships. However, current approaches lack statistical reliability assurances, limiting their practical utility for selecting proteins for further experimental and in-silico characterization. To address this challenge, we introduce a statistically principled approach to protein search leveraging principles from conformal prediction, offering a framework that ensures statistical guarantees with user-specified risk and provides calibrated probabilities (rather than raw ML scores) for any protein search model. Our method (1) lets users select many biologically-relevant loss metrics (i.e. false discovery rate) and assigns reliable functional probabilities for annotating genes of unknown function; (2) achieves state-of-the-art performance in enzyme classification without training new models; and (3) robustly and rapidly pre-filters proteins for computationally intensive structural alignment algorithms. Our framework enhances the reliability of protein homology detection and enables the discovery of uncharacterized proteins with likely desirable functional properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Sea ice evolution along the Northern Sea Route and implications for trans-Arctic shipping from 2021 through 2060

Arctic surface temperatures warmed at twice the global average in the second half of the 20 th century due to Arctic amplification (AA), a phenomenon predominantly caused by regional polar changes, like the melting of perennial sea ice and reduced sea ice extent (leading to more solar radiation being absorbed by the ocean surface as opposed to being reflected back to space by the ice surface). AA is projected to reach a factor of three even if the climate is stabilized by the mid 21 st century by reduced greenhouse gas emissions. In all emissions scenarios, AA is projected to lead to temperature changes at least 2.4 times larger than global mean surface temperature changes occurring between 2070 and 2100. The ice-albedo feedback, which occurs when the polar-marine surface absorbs more radiation as highly reflective sea ice melts, is reversible such that the premise of a runaway process is no longer accepted as a realistic possibility. No matter what actions are taken to reduce CO 2 concentrations in the atmosphere from now on, two different methods of predicting an ice-free Arctic suggest that perennial sea ice will mostly disappear in September by the year 2050. If there is no reduction in anthropogenic CO 2 and methane emissions, that scenario could occur sooner than 2030. Defining Arctic navigability as safe and economic passage of Polar Class 7 cargo ships without need of an escorting icebreaker, no single trans Arctic ship route will be navigable year-round in the first half of the 21 st century, including in the strong emission scenarios. However, seasonal trans-Arctic navigability will increase this century. An estimate on the number of days per year that the Northern See Route (NSR) will be navigable in the future is beyond the scope of this report. Along Northern Sea Routes 5 and 6, which run close to the Russian coast and Yamal LNG plant, an ARC 7 ice class LNG tanker, the equivalent of a Polar Class 3 (PC3) vessel, will be at low risk in December through April at some point during the current decade. May will continue to entail some risk (more than April) along relatively short segments of these routes through the end of the next decade (2030-2039). Come June, snow rapidly melts away, and thereafter the underlying sea ice begins becomes thinner and less concentrated, greatly reducing risk. However, neither path is desirable for ARC 7 tankers due to shallow bathymetry in Sannikov Strait, and a more desirable path for these ships passes to the north of the New Siberian Islands (route 20, discussed further below). Conventional LNG tankers (i.e., non-ice-strengthened vessels according to the IMO classification), without icebreaker escorts, will continue to encounter dangerous or impassable conditions along many sections of the NSR through the end of this decade for many months of the year. Through 2039, these conventional tankers will be able to operate safely along NSR 6 from August through October. By 2040-2049, the span of safe operation increases to August through November, and by 2050-2059 it increases to July through November, assuming a northward deviation to avoid the Sannikov Strait.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

The Ejectable Data Recorder: A Lean, Risk-Informed Approach for Hardware Development

NASA is developing the Orion spacecraft to transport crew from the Earth to the Moon as part of the Artemis series of missions. To provide a crew escape capability from pre-launch through ascent, the Orion vehicle is equipped with a Launch Abort System (LAS), built by Lockheed Martin, which pulls the capsule away from the launch vehicle in the event of an abort scenario. The Ascent Abort 2 (AA-2) test flight occurred on July 2, 2019,and tested a production version of the LAS to ensure that it can operate as intended, and to collect a large data set from hundreds of sensors on the vehicle to support Orion flight certification. In the original AA-2 architecture, a single-string set of communications antennas on the LAS would downlink all of the in-flight test data to ground stations. However, that communications architecture was predicted to have data dropouts during abort and jettison of the LAS, and would not support data transmission at all after LAS jettison. As a result, a comprehensive trade study was completed, yielding the addition of antennas on the crew module (CM), a buffer/rebroadcast capability for key portions of the flight, and an ejectable data recorder (EDR) subsystem. This EDR subsystem would serve as a backup to the radio frequency (RF) communications system, and would be non-flight critical, providing a unique capability that enabled management to take a different approach with the hardware and software development. The Crew Module and Separation Ring were developed as “Class 1”Flight Hardware, albeit with some tailoring approaches to enable efficiencies. The Class 1 designation requires full rigor for flight hardware and software, documenting everything that happens to a piece of hardware from procurement through disposal, requiring a full spectrum of acceptance tests, and the highest rigor of quality assurance processes. At the other end of the spectrum, Class 3hardware is controlled, but not intended for flight, and leaves the level of rigor up to the project manager. This classification is often used for research and development projects. Similarly,Class-1E has been recently defined at NASA for ISS payloads and technology development projects that are not flight critical and do not need the full rigor of Class 1 to be successful. The EDR subsystem was challenged at commencement to adopt a skunkworks and agile-like approach to hardware development, allowing for a different risk posture than the rest of the AA-2 hardware. After initially pursuing Class 1 processes, the EDR subsystem design evolved to incorporating numerous commercial components, leading to re-designation as a Class-1E subsystem. The resulting EDR subsystem was fully successful in meeting all flight system requirements, and achieved 100% retrieval of flight test data. This paper will discuss the risk posture of the EDR subsystem and the subsequent tailoring that was enacted as part of its Class-1E status.

EDR↗
DOE OSTI2019

Impact of Preoperative Skeletal Muscle Mass and Nutritional Status on Short-and Long-Term Outcomes After Esophagectomy for Esophageal Cancer: A Retrospective Observational Study

Background: Preoperative muscle volume and body mass index (BMI) are associated with postoperative outcomes. Because esophagectomy for esophageal cancer (EC) is associated with high morbidity and because EC has a poor prognosis, this study investigated the association of preoperative skeletal muscle mass and nutritional status with postoperative outcomes. Methods: The study analyzed 483 patients who underwent esophagectomy from 2009 to 2012. The cross-sectional area of the psoas muscle index (PMI) was measured at the third lumbar vertebral level using computed tomography. Clavien–Dindo classifications were used to analyze postoperative complications. Because skeletal muscle mass varies according to sex, all analyses were performed accordingly (390 males, 93 females). Results: For male patients, BMI was a significant multivariate factor, and PMI, a univariate factor, predicted postoperative complications and overall survival (OS). Using a preoperative nutritional and muscular (PNM) score derived from BMI and PMI results (patients were allocated 1 point if their BMI was < 18.5 kg/m{sup 2} and 1 point if their PMI was < 600 mm{sup 2}/m{sup 2}, for a possible maximum total of 2 points), male patients were categorized as high risk (score 2), moderate risk (score 1), or low risk (score 0). In the low-risk group, anastomotic leakage was significantly less (p = 0.01), and the 3-year OS was significantly better (p < 0.01). On the other hand, in female patients, neither BMI nor PMI was a significant factor for postoperative outcomes. Conclusions: For male patients, the PNM score is a promising tool for predicting postoperative outcomes and identifying patients requiring preoperative nutritional intervention and rehabilitation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗