Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “personal”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Large Individual Ion FTICR Measurements from the Mid-1990s Using Reactions for Charge Determination Mass Spectrometry

This article discusses from a personal and present-day perspective the first studies of large highly charged individual molecular ions that were conducted using electrospray ionization (ESI) with Fourier transform ion cyclotron resonance (FTICR) MS in the mid-1990s. These studies are distinguished from current Charge Detection Mass Spectrometry (CDMS) research primarily by their use of individual ion charge state changes due to reactions. Here this work describes the key differences in technologies and methods with present CDMS, and the likely implications of these differences. I also comment on surprising individual ion behavior observed in some measurements involving increases in charge state, as well as their possible basis, and also briefly discuss the potential utility of the reaction-based mass measurement approach used in the context of what might more globally be referred to as ‘Charge Determination Mass Spectrometry’.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Exocortex Network for AI-Augmented Human-Led Scientific Expedition

AI advances in science can be viewed along two main directions with a fluid boundary: enhancing efficiency through automation and smart tools to accelerate tasks that humans can already perform; and enabling exploration into uncharted territories and potentially toward AGI. These advances manifest in the AI cognitive core through the development and explainability of foundation models; in the physical embodiment of instruments and facilities; and in the integrated agency of AI workflows exemplified by the science exocortex. To address the role of humans in this evolving landscape, in this Perspective, we suggest a third direction: the development of personalized agents that form human-centered networks, supporting both efficiency and exploration while ensuring that AI remains aligned with human vision.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Deadly Heat Stress to Become Commonplace Across South Asia Already at 1.5°C of Global Warming

South Asia (SA) is one of those hotspots where earliest exposure to deadly wet-bulb temperatures (Tw >35°C) is projected in warmer future climates. Here we find that even today parts of SA experience the upper limits of labor productivity (Tw >32°C) or human survivability (Tw >35°C), indicating that previous estimates for future exposure to Tw-based extremes may be conservative. Our results show that at 2°C global warming above pre-industrial levels, the per person exposure approximately increases by 2.2 (2.7) folds for unsafe labor (lethal) threshold compared to the 2006–2015 reference period. Limiting warming to 1.5°C would avoid about half that impact. The population growth under the middle-of-the-road socioeconomic pathway could further increase these exposures by a factor of ~2 by the mid-century. These results indicate an imminent need for adaptation measures, while highlighting the importance of stringent Paris-compatible mitigation actions for limiting future emergence of such conditions in SA.

58 GEOSCIENCES↗
DOE OSTI2025

Dual‐Transformer Deep Learning Framework for Seasonal Forecasting of Great Lakes Water Levels

Abstract The Great Lakes of North America form one of the largest freshwater systems on Earth, and their lake‐wide average water levels (lake levels) can fluctuate by more than 0.5 m on a seasonal scale. These fluctuations pose substantial challenges for coastal resilience, flood risk management, and navigation planning. Accurate seasonal forecasting of lake levels using traditional mechanistic models is challenging due to the complex physical mechanisms and coupled hydroclimatic processes involved. Recently, deep learning has gained prominence in geoscience applications for its ability to recognize intricate patterns within multiphysical data sets. Here, we introduce a novel Dual‐Transformer deep learning framework, tested on the Great Lakes. This architecture integrates two modified Transformer models: the Prophet, which predicts underlying trends, and the Critic, which refines the Prophet's predictions. The final lake level prediction is derived by weighting the outputs of both models through a multi‐layer perceptron, jointly trained with the Prophet and Critic to enhance overall accuracy. Our results demonstrate that the innovative learning framework achieves the highest prediction accuracy compared to established deep learning models when using identical input features. It attains a root mean square error of 4–7 cm in predicting lake levels up to 6 months in advance across the lakes. Additionally, the Dual‐Transformer model runs six orders of magnitude faster than conventional mechanistic models, producing results in less than one second on a typical personal computer. These findings suggest that our deep learning framework has strong potential to advance lake level prediction and carries important implications for water management and disaster mitigation, thereby enhancing the quality of life in coastal regions.

Chen, Yi [Great Lakes Research Center Michigan Tec↗
DOE OSTI2020

A reflection on lithium-ion battery cathode chemistry

Abstract Lithium-ion batteries have aided the portable electronics revolution for nearly three decades. They are now enabling vehicle electrification and beginning to enter the utility industry. The emergence and dominance of lithium-ion batteries are due to their higher energy density compared to other rechargeable battery systems, enabled by the design and development of high-energy density electrode materials. Basic science research, involving solid-state chemistry and physics, has been at the center of this endeavor, particularly during the 1970s and 1980s. With the award of the 2019 Nobel Prize in Chemistry to the development of lithium-ion batteries, it is enlightening to look back at the evolution of the cathode chemistry that made the modern lithium-ion technology feasible. This review article provides a reflection on how fundamental studies have facilitated the discovery, optimization, and rational design of three major categories of oxide cathodes for lithium-ion batteries, and a personal perspective on the future of this important area.

Science & Technology - Other Topics↗
DOE OSTI2022

Mitigation of SARS-CoV-2 transmission at a large public university

In Fall 2020, universities saw extensive transmission of SARS-CoV-2 among their populations, threatening health of the university and surrounding communities, and viability of in-person instruction. Here we report a case study at the University of Illinois at Urbana-Champaign, where a multimodal “SHIELD: Target, Test, and Tell” program, with other non-pharmaceutical interventions, was employed to keep classrooms and laboratories open. The program included epidemiological modeling and surveillance, fast/frequent testing using a novel low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD, and digital tools for communication and compliance. In Fall 2020, we performed >1,000,000 covidSHIELD tests, positivity rates remained low, we had zero COVID-19-related hospitalizations or deaths amongst our university community, and mortality in the surrounding Champaign County was reduced more than 4-fold relative to expected. This case study shows that fast/frequent testing and other interventions mitigated transmission of SARS-CoV-2 at a large public university.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Molecular basis of differential HLA class I-restricted T cell recognition of a highly networked HIV peptide

Cytotoxic-T-lymphocyte (CTL) mediated control of HIV-1 is enhanced by targeting highly networked epitopes in complex with human-leukocyte-antigen-class-I (HLA-I). However, the extent to which the presenting HLA allele contributes to this process is unknown. Here we examine the CTL response to QW9, a highly networked epitope presented by the disease-protective HLA-B57 and disease-neutral HLA-B53. Despite robust targeting of QW9 in persons expressing either allele, T cell receptor (TCR) cross-recognition of the naturally occurring variant QW9_S3T is consistently reduced when presented by HLA-B53 but not by HLA-B57. Crystal structures show substantial conformational changes from QW9-HLA to QW9_S3T-HLA by both alleles. The TCR-QW9-B53 ternary complex structure manifests how the QW9-B53 can elicit effective CTLs and suggests sterically hindered cross-recognition by QW9_S3T-B53. We observe populations of cross-reactive TCRs for B57, but not B53 and also find greater peptide-HLA stability for B57 in comparison to B53. These data demonstrate differential impacts of HLAs on TCR cross-recognition and antigen presentation of a naturally arising variant, with important implications for vaccine design.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins

Abstract Metastasis is the leading cause of death in patients with cancer, driving considerable scientific and clinical interest in immunosurveillance of micrometastases. We investigated this process by creating a multiscale mathematical model to study the interactions between the immune system and the progression of micrometastases in general epithelial tissue. We analyzed the parameter space of the model using high-throughput computing resources to generate over 100,000 virtual patient trajectories. We demonstrated that the model could recapitulate a wide variety of virtual patient trajectories, including uncontrolled growth, partial response, and complete immune response to tumor growth. We classified the virtual patients and identified key patient parameters with the greatest effect on the simulated immunosurveillance. We highlight the lessons derived from this analysis and their impact on the nascent field of cancer patient digital twins (CPDTs). While CPDTs could enable clinicians to systematically dissect the complexity of cancer in each individual patient and inform treatment choices, our work shows that key challenges remain before we can reach this vision. In particular, we show that there remain considerable uncertainties in immune responses, unreliable patient stratification, and unpredictable personalized treatment. Nonetheless, we also show that in spite of these challenges, patient-specific models suggest strategies to increase control of clinically undetectable micrometastases even without complete parameter certainty.

Mathematical & Computational Biology↗
DOE OSTI2020

A robust benchmark for detection of germline large deletions and insertions

New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution and comprehensiveness. To help translate these methods to routine research and clinical practice, we developed a sequence-resolved benchmark set for identification of both false-negative and false-positive germline large insertions and deletions. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle Consortium integrated 19 sequence-resolved variant calling methods from diverse technologies. The final benchmark set contains 12,745 isolated, sequence-resolved insertion (7,281) and deletion (5,464) calls ≥50 base pairs (bp). The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.51 Gbp and 5,262 insertions and 4,095 deletions supported by ≥1 diploid assembly. We demonstrate that the benchmark set reliably identifies false negatives and false positives in high-quality SV callsets from short-, linked- and long-read sequencing and optical mapping.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Fast and accurate metagenotyping of the human gut microbiome with GT-Pro

Single nucleotide polymorphisms (SNPs) in metagenomics are used to quantify population structure, track strains and identify genetic determinants of microbial phenotypes. However, existing alignment-based approaches for metagenomic SNP detection require high-performance computing and enough read coverage to distinguish SNPs from sequencing errors. To address these issues, we developed the GenoTyper for Prokaryotes (GT-Pro), a suite of methods to catalog SNPs from genomes and use unique k-mers to rapidly genotype these SNPs from metagenomes. Compared to methods that use read alignment, GT-Pro is more accurate and two orders of magnitude faster. Here, using high-quality genomes, we constructed a catalog of 104 million SNPs in 909 human gut species and used unique k-mers targeting this catalog to characterize the global population structure of gut microbes from 7,459 samples. GT-Pro enables fast and memory-efficient metagenotyping of millions of SNPs on a personal computer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Risk of hematological malignancies from CT radiation exposure in children, adolescents and young adults

Over one million European children undergo computed tomography (CT) scans annually. Although moderate- to high-dose ionizing radiation exposure is an established risk factor for hematological malignancies, risks at CT examination dose levels remain uncertain. Here we followed up a multinational cohort (EPI-CT) of 948,174 individuals who underwent CT examinations before age 22 years in nine European countries. Radiation doses to the active bone marrow were estimated on the basis of body part scanned, patient characteristics, time period and inferred CT technical parameters. We found an association between cumulative dose and risk of all hematological malignancies, with an excess relative risk of 1.96 (95% confidence interval 1.10 to 3.12) per 100 mGy (790 cases). Similar estimates were obtained for lymphoid and myeloid malignancies. Results suggest that for every 10,000 children examined today (mean dose 8 mGy), 1–2 persons are expected to develop a hematological malignancy attributable to radiation exposure in the subsequent 12 years. Our results strengthen the body of evidence of increased cancer risk at low radiation doses and highlight the need for continued justification of pediatric CT examinations and optimization of doses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A multi-ancestry genetic study of pain intensity in 598,339 veterans

Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects the quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids had a central role in precipitating the opioid crisis. Despite an estimated heritability of 25–50%, the genetic architecture of chronic pain is not well-characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 126 independent genetic loci, 69 of which are new. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level and cognitive traits. Integration of the genome-wide association studies findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, β-blockers and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A high-fidelity residential building occupancy detection dataset

Abstract This paper describes development of a data acquisition system used to capture a range of occupancy related modalities from single-family residences, along with the dataset that was generated. The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally identifiable information; indoor environmental readings, captured every ten seconds; and ground truth binary occupancy status. The data acquisition system, coined the mobile human presence detection (HPDmobile) system, was deployed in six homes for a minimum duration of one month each, and captured all modalities from at least four different locations concurrently inside each home. The environmental modalities are available as captured, but to preserve the privacy and identity of the occupants, images were downsized and audio files went through a series of processing steps, as described in this paper. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Non-invasive characterization of complex coronary lesions

Conventional invasive diagnostic imaging techniques do not adequately resolve complex Type B and C coronary lesions, which present unique challenges, require personalized treatment and result in worsened patient outcomes. These lesions are often excluded from large-scale non-invasive clinical trials and there does not exist a validated approach to characterize hemodynamic quantities and guide percutaneous intervention for such lesions. This work identifies key biomarkers that differentiate complex Type B and C lesions from simple Type A lesions by introducing and validating a coronary angiography-based computational fluid dynamic (CFD-CA) framework for intracoronary assessment in complex lesions at ultrahigh resolution. Among 14 patients selected in this study, 7 patients with Type B and C lesions were included in the complex lesion group including ostial, bifurcation, serial lesions and lesion where flow was supplied by collateral bed. Simple lesion group included 7 patients with lesions that were discrete, <10mm long and readily accessible. Intracoronary assessment was performed using CFD-CA framework and validated by comparing to clinically measured pressure-based index, such as FFR. Local pressure, endothelial shear stress (ESS) and velocity profiles were derived for all patients. We validates the accuracy of our CFD-CA framework and report excellent agreement with invasive measurements (n=14,R 2 =0.6,p=0.0013). Ultra-high resolution achieved by the model enable physiological assessment in complex lesions and quantify hemodynamic metrics in all vessels up to 1mm in diameter. Importantly, we demonstrate that in contrast to traditional pressure-based metrics, there is a significant difference in the intracoronary hemodynamic forces, such as ESS, in complex lesions compared to simple lesions at both resting and hyperemic physiological states [n = 14, p=0.03]. Higher ESS was observed in the complex lesion group (7.0±4.7 Pa) than in simple lesion group (4.8±3.6 Pa). Complex coronary lesions have higher ESS compared to simple lesions, such differential hemodynamic evaluation can provide much the needed insight into the increase in adverse outcomes for such patients and has incremental prognostic value over traditional pressure-based indices, such as FFR.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Survival analysis of localized prostate cancer with deep learning

In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics C td over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved C td 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained C td 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Air-conditioning adoption and electricity demand highlight climate change mitigation–adaptation tradeoffs

Abstract We elucidate mid-century climate change impacts on electricity demand accounting for endogenous adoption of residential air-conditioning (AC) in affluent, cooler countries in Europe, and in poorer, hotter states in India. By 2050, in a high-warming scenario (SSP585) AC prevalence grows twofold in Europe and fourfold in India, reaching around 40% in both regions. We document a mitigation-adaptation tradeoff: AC expansion reduces daily heat exposures by 150 million and 3.8 billion person degree-days (PDDs), but increases annual electricity demand by 34 TWh and 168 TWh in Europe and India, respectively (corresponding to 2% and 15% of today’s consumption). The increase in adoption and use of AC would result in an additional 130 MMTCO2, of which 120 MMTCO2 in India alone, if the additional electricity generated were produced with today’s power mix. The tradeoff varies geographically and across income groups: a one PDD reduction in heat exposure in Europe versus India necessitates five times more electricity (0.53 kWh vs 0.1 kWh) and two times more emissions (0.16 kgCO $$_2$$ 2 vs 0.09 kgCO $$_2$$ 2 ), on average. The decomposition of demand drivers offers important insights on how such tradeoff can be moderated through policies promoting technology-based and behavioral-based adaptation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Traumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict. Major challenges to developing personalized treatment for TBI include distilling large quantities of complex data and increasing the precision with which patient outcome prediction (prognoses) can be rendered. We developed and applied interpretable machine learning methods to TBI patient data. We show that complex data describing TBI patients' intake characteristics and outcome phenotypes can be distilled to smaller sets of clinically interpretable latent factors. We demonstrate that 19 clusters of TBI outcomes can be predicted from intake data, a ~ 6× improvement in precision over clinical standards. Finally, we show that 36% of the outcome variance across patients can be predicted. These results demonstrate the importance of interpretable machine learning applied to deeply characterized patients for data-driven distillation and precision prognosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

ToF-SIMS evaluation of PEG-related mass peaks and applications in PEG detection in cosmetic products

Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.

36 MATERIALS SCIENCE↗