Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Vital signs”

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.

18 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Background: Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU. Objective: We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. Methods: We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results: We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions: The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Xanthohumol Microbiome and Signature in Healthy Adults (the XMaS Trial): Safety and Tolerability Results of a Phase I Triple-Masked, Placebo-Controlled Clinical Trial

Scope Xanthohumol, a prenylflavonoid from hops, has been extensively studied preclinically but has undergone limited research in human subjects. A triple-masked, placebo-controlled phase I clinical trial was conducted to examine the safety and tolerability of xanthohumol. Methods and results Thirty healthy volunteers were randomized to 24?mg/day xanthohumol (99.8% pure) or placebo for eight weeks. Comprehensive metabolic panels, complete blood counts, body weight, vital signs, and health-related quality of life questionnaires were assessed every two weeks. Participants were interviewed for adverse events (AEs) throughout the trial. Thirteen of 14 (93%) and 14 of 16 (88%) participants completed the trial in Group A and B respectively. There were no withdrawals due to AEs. There were no clinically relevant, between-group differences in laboratory biomarkers, body weight, vital signs, or health-related quality of life. There were no severe or FDA-defined serious AEs, but non-serious AEs were documented in both Group A (n=42) and Group B (n=58). Conclusion Over an eight-week period, 24 mg daily xanthohumol was safe and well-tolerated by healthy adults.

Langley, Blake↗
DOE OSTI2020

Wearable radio-frequency sensing of respiratory rate, respiratory volume, and heart rate

Abstract Many health diagnostic systems demand noninvasive sensing of respiratory rate, respiratory volume, and heart rate with high user comfort. Previous methods often require multiple sensors, including skin-touch electrodes, tension belts, or nearby off-the-body readers, and hence are uncomfortable or inconvenient. This paper presents an over-clothing wearable radio-frequency sensor study, conducted on 20 healthy participants (14 females) performing voluntary breathing exercises in various postures. Two prototype sensors were placed on the participants, one close to the heart and the other below the xiphoid process to couple to the motion from heart, lungs and diaphragm, by the near-field coherent sensing principle. We can achieve a satisfactory correlation of our sensor with the reference devices for the three vital signs: heart rate ( r = 0.95), respiratory rate ( r = 0.93) and respiratory volume ( r = 0.84). We also detected voluntary breath-hold periods with an accuracy of 96%. Further, the participants performed a breathing exercise by contracting abdomen inwards while holding breath, leading to paradoxical outward thorax motion under the isovolumetric condition, which was detected with an accuracy of 83%.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Integrating multimodal data through interpretable heterogeneous ensembles

Motivation: Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results: We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Pre-Symptomatic COVID Screening

Temperature checks for fever are extensively used for preliminary COVID screenings but are ineffective during the incubation stage of infection when a person is asymptotic. Researchers at the European Centre for Disease Prevention and Control concluded that approximately 75% of passengers infected with COVID-19 and traveling from affected Chinese cities would not be detected by early screening. Core body temperature is normally kept within a narrow range and has the smallest relative standard deviation of all vital signs. Heat in the body is prioritized around internal organs at the expense of the periphery by controlling blood flow. In fact, blood flow to the skin may vary by a factor of 100 depending on thermal conditions. This adaptation causes rapid temperature fluctuations in different skin regions from changes in cardiac output, metabolism, and likely cytokine diffusion during inflammation that would not be seen in average core body temperature. Current IR and thermal scanners used for temperature checks are not necessarily reflective of core body temperatures and require cautious interpretation as they frequently result in false positive and false negative diagnosis. Hand held thermometers measure average skin temperatures and can get readings that differ from core body temperature by as much as 7°. Rather than focusing on a core body temperature threshold assessment we believe that variability of temperature patterns using a novel wearable transdermal microneedle sensor will be more sensitive to infections in the incubation stage and propose to develop a wearable transdermal temperature sensor using established Sandia microneedle technology for pre-symptomatic COVID screening that can additionally be used to monitor disease progression at later stages.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Pre Symptomatic COVID Screening

Temperature checks for fever are extensively used for preliminary COVID screenings but are ineffective during the incubation stage of infection when a person is asymptotic. Researchers at the European Centre for Disease Prevention and Control concluded that approximately 75% of passengers infected with COVID-19 and traveling from affected Chinese cities would not be detected by early screening. Core body temperature is normally kept within a narrow range and has the smallest relative standard deviation of all vital signs. Heat in the body is prioritized around internal organs at the expense of the periphery by controlling blood flow. In fact, blood flow to the skin may vary by a factor of 100 depending on thermal conditions. This adaptation causes rapid temperature fluctuations in different skin regions from changes in cardiac output, metabolism, and likely cytokine diffusion during inflammation that would not be seen in average core body temperature. Current IR and thermal scanners used for temperature checks are not necessarily reflective of core body temperatures and require cautious interpretation as they frequently result in false positive and false negative diagnosis. Hand held thermometers measure average skin temperatures and can get readings that differ from core body temperature by as much as 7°. Rather than focusing on a core body temperature threshold assessment we believe that variability of temperature patterns using a novel wearable transdermal microneedle sensor will be more sensitive to infections in the incubation stage and propose to develop a wearable transdermal temperature sensor using established Sandia microneedle technology for pre symptomatic COVID screening that can additionally be used to monitor disease progression at later stages.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability

Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient’s status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Wearable health monitoring device

A wearable patient device is provided that includes one or more sensors. The one or more sensors can record one or both of ECG information or phonocardiographic information. The sensor information can be used to determine the blood pressure of a monitored individual, including on a continuous basis. Blood pressure can be determined using one or both of a determined time to empty or fill one or more heart chambers or first and second blood velocities. Vital sign information can be provided to a monitoring individual, including graphical representations of trend information.

Gosink, Luke J.↗
DOE OSTI2025

3D-Printed mmWave Quasi-Holographic Antenna for 2-D Beamforming

This paper presents the design and manufacturing of a novel 2D-scanning antenna that integrates a 3D-printed Rotman lens with a quasi-holographic leaky-wave antenna (HLWA). The proposed design achieved beam-scanning capabilities by leveraging the beamforming of the Rotman lens and the high-gain directive properties of the quasi-HLWA. The Rotman lens (RL) enables beam steering in the elevation plane by switching between input ports. The quasi-HLWA, designed using holographic principles, achieves frequency-controlled beam scanning in the azimuth plane. The entire antenna structure was fabricated using additive manufacturing with an Ink1092 substrate and silver ink for the conductive traces. This approach provides greater control over material placement and design freedom compared to traditional methods. A 25° transmission linear substrate taper was used to ensure good impedance matching between the Rotman lens and the quasi-HLWA, allowing greater gain while maintaining a good scanning range. The experimental results validate the 2D scanning capability of the proposed antenna. The antenna system provides coverage from −54° to 54° in the elevation θ plane and −28° to 28° in the azimuth plane ϕ , with a maximum measured gain of 21.3 dBi at 28 GHz with an average radiation efficiency η=60 %. The fabricated prototype was tested, and the performance was in good agreement with the simulated performance.

Rotman lens↗
DOE OSTI2025

Evaluating the impacts of Variable Message Signs on Airport Curbside Performance Using Microsimulation

Curbs play a vital role in facilitating vehicle access and egress for individuals at airports. Inefficiently allocating this resource hinders airport accessibility and productivity, resulting in congestion, longer travel times, and increased pollution. As airport demand fluctuates throughout the day and grows over time, airports face intensified curbside pressure. Yet, curb management research is significantly less robust at airports than in urban areas. Given the unbalanced nature of airport demand—riders tend to arrive simultaneously at specific entrances at certain hours—Variable Message Sign (VMS) arises as a cost-effective technology to divert vehicles from congested to underutilized curbs. Still, VMS implementation faces a significant challenge. Historically, airports have managed VMS heuristically and by intuition rather than an evidence-based approach. This research investigates the impacts of implementing VMS on curb performance at airports. By considering different driver compliance rates (DCR), we aim to determine when the sign should be turned on and off to diverge traffic to avoid undesired externalities while enhancing curb performance. Using a validated agent-based microsimulation model, VISSIM, we analyzed the Seattle-Tacoma (SeaTac) Airport as a case study. We modeled sixteen VMS management scenarios and a baseline where the message sign is not displayed, diverging vehicles between the departures and arrivals access levels at four different moments (early morning, morning, afternoon, and late night). We quantified the effects of VMS using seven metrics, including curb productivity index (CPI), curb accessibility (CA), queue length, queue duration, delay, vehicle counts, and emissions. The results of each scenario were compared against the baseline using absolute and relative changes and Repeated Measures ANOVA. Overall, VMS improved curb performance and traffic conditions at the airport, reducing emissions by 14.8% to 8.9%. Moreover, significant reductions in queue length (1,150 ft to 100 ft) and duration (15 to 144 minutes) were observed in the sending link under all VMS policies. However, impacts on the receiving link varied based on congestion, with significant increases in queue duration (9.8 to 24 min) when congested but no substantial changes in free flow. Notably, diverging vehicles to congested links resulted in non-significant results, and activating late and deactivating late VMS affected curb productivity (-5.8% to -61.4%), curb accessibility (-16.5% to -25.8%), cumulative counts (-33.4% to -59.4%), and vehicle delay (95.98% to 594.3%). Activating VMS before congestion begins in the sending link and deactivating before a queue forms in the receiving link yield the most significant improvements: 8.1% to 10.1% in CPI, 9.4% to9.6% in CA, -29.3% to -77.9% in total delay, -11.6% to -13.9% in total emissions, and 101% to 103% in cumulative counts. As the analysis was made with a wide range of time periods, access levels, driver compliance rates, and scenarios, we believe our findings can provide valuable insights into how airports should manage VMS. Our work introduces a novel approach to the scientific airport literature, as some of our metrics were previously unexplored. Additionally, we propose a methodology that other airports can adopt to maximize their curb performance.

Gutierrez, Jorge D.↗
DOE OSTI2023

Single-dose mucosal replicon-particle vaccine protects against lethal Nipah virus infection up to 3 days after vaccination

Nipah virus (NiV) causes a highly lethal disease in humans who present with acute respiratory or neurological signs. No vaccines against NiV have been approved to date. Here, we report on the clinical impact of a novel NiV-derived nonspreading replicon particle lacking the fusion (F) protein gene (NiVΔF) as a vaccine in three small animal models of disease. A broad antibody response was detected that included immunoglobulin G (IgG) and IgA subtypes with demonstrable Fc-mediated effector function targeting multiple viral antigens. Single-dose intranasal vaccination up to 3 days before challenge prevented clinical signs and reduced virus levels in hamsters and immunocompromised mice; decreases were seen in tissues and mucosal secretions, critically decreasing potential for virus transmission. This virus replicon particle system provides a vital tool to the field and demonstrates utility as a highly efficacious and safe vaccine candidate that can be administered parenterally or mucosally to protect against lethal Nipah disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The straw tracking detector for the Fermilab Muon g-2 Experiment

The Muon g-2 Experiment at Fermilab uses a gaseous straw tracking detector to make detailed measurements of the stored muon beam profile, which are essential for the experiment to achieve its uncertainty goals. Positrons from muon decays spiral inward and pass through the tracking detector before striking an electromagnetic calorimeter. The tracking detector is therefore located inside the vacuum chamber in a region where the magnetic field is large and non-uniform. As such, the tracking detector must have a low leak rate to maintain a high-quality vacuum, must be non-magnetic so as not to perturb the magnetic field and, to minimize energy loss, must have a low radiation length. The performance of the tracking detector has met or surpassed the design requirements, with adequate electronic noise levels, an average straw hit resolution of (110 ± 20) μm, a detection efficiency of 97% or higher, and no performance degradation or signs of aging. The tracking detector's measurements result in an otherwise unachievable understanding of the muon's beam motion, particularly at early times in the experiment's measurement period when there are a significantly greater number of muons decaying. This is vital to the statistical power of the experiment, as well as facilitating the precise extraction of several systematic corrections and uncertainties. This paper describes the design, construction, testing, commissioning, and performance of the tracking detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Time irreversibility as an indicator of approaching tipping points in Earth subsystems

With shifting environmental trends, many Earth system elements may be poised to undergo critical transitions or ‘tipping’. Reliable anticipation of these tipping elements is vital to inform policy decisions. Many of the current methods for tipping point detection are based on loss of resilience or ‘critical slowdown’ of the system as it approaches a tipping point. However, these methods are prone to false alarms; the detected slowdown may be an artifact of nonstationary noise unrelated to tipping behavior. Here, we explore the efficacy of early warning signs based on a nonequilibrium thermodynamics framework. The model-free detection method relies on the increased intrinsic time-irreversibility due to detailed balance breaking, preceding the onset of tipping or instabilities. We demonstrate that these EWSs are effective for tipping point detection and robust against false alarms due to nonstationary noise, using idealized models for two key elements of the Earth system that are prone to tipping: the Atlantic Meridional Overturning Circulation and Arctic sea-ice loss.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Hyperfine and quadrupole interactions for Dy isotopes in DyPc 2 molecules

Nuclear spin levels play a vital role in understanding magnetization dynamics and implementation and control of quantum bits in lanthanide-based single-molecule magnets. We investigate the hyperfine and nuclear quadrupole interactions for 161 Dy and 163 Dy nuclei in anionic DyPc 2 (Pc=phthalocyanine) single-molecule magnets, using multiconfigurational ab initio methods (beyond density-functional theory) including spin–orbit interaction. The two isotopes of Dy are chosen because the others have zero nuclear spin. Both isotopes have the nuclear spin I =5/2, although the magnitude and sign of the nuclear magnetic moment differ from each other. The large energy gap between the electronic ground and first-excited Kramers doublets, allows us to map the microscopic hyperfine and quadrupole interaction Hamiltonian onto an effective Hamiltonian with an electronic pseudo-spin that corresponds to the ground Kramers doublet. Our ab initio calculations illustrate that the coupling between the nuclear spin and electronic orbital angular momentum contributes the most to the hyperfine interaction and that both the hyperfine and nuclear quadrupole interactions for 161 Dy and 163 Dy nuclei are much smaller than those for the 159 Tb nucleus in TbPc 2 single-molecule magnets. The calculated separations of the electronic-nuclear levels are comparable to experimental data reported for 163 DyPc 2 . We demonstrate that hyperfine interaction for the Dy Kramers ion leads to tunnel splitting (or quantum tunneling of magnetization) at zero field. This effect does not occur for TbPc 2 single-molecule magnets. The magnetic field values of the avoided level crossings for 161 DyPc 2 and 163 DyPc 2 are found to be noticeably different, which can be observed from the experiment.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau

With the highest albedo of the land surface, snow plays a vital role in Earth's surface energy budget and water cycle. Snow albedo is primarily controlled by snow grain properties (e.g., size and shape) and light-absorbing particles (LAPs) such as black carbon (BC) and dust. The mixing state of LAPs in snow also has impacts on LAP-induced snow albedo reduction and surface radiative forcing (RF). However, most land surface models assume that snow grain shape is spherical and LAPs are externally mixed with the snow grains. This study improves the snow radiative transfer model in the Energy Exascale Earth System Model version 2.0 (E3SM v2.0) Land Model (ELM v2.0) by considering non-spherical snow grain shapes (i.e., spheroid, hexagonal plate, and Koch snowflake) and internal mixing of dust–snow, and it systematically evaluates the impacts on the surface energy budget and water cycle over the Tibetan Plateau (TP). A series of ELM simulations with different treatments of snow grain shape, mixing state of BC–snow and dust–snow, and sub-grid topographic effects (TOP) on solar radiation are performed. Compared with two remote sensing snow products derived from the Moderate Resolution Imaging Spectroradiometer, the control ELM simulation (ELM_Control) with the default configurations of spherical snow grain shape, internal mixing of BC–snow, external mixing of dust–snow, and without TOP as well as the ELM simulation with new model features (ELM_New) can both capture the overall snow distribution reasonably. Additionally, ELM_New overall shows smaller biases in snow cover fraction than ELM_Control in spring when snowmelt is important for water management. The estimated LAP-induced RF in ELM_New ranges from 0 to 19.3 W m -2 with the area-weighted average value of 1.5 W m -2 that is comparable to the reported values in existing studies. The Koch snowflake shape, among other non-spherical shapes, shows the largest difference from the spherical shape in spring when snow processes related to the surface energy budget and water cycle have high importance. The impacts of the mixing state of LAP in snow are smaller than the shape effects and depend on snow grain shape. Compared to external mixing, internal mixing of LAP–snow can lead to larger snow albedo reduction and snowmelt, which further affect the surface energy budget and water cycle. The individual contributions of non-spherical snow shape, mixing state of LAP–snow, and local topography impacts on the snow and surface fluxes have different signs and magnitudes, and their combined effects may be negative or positive due to complex and nonlinear interactions among the factors. Overall, the changes in net solar radiation in spring due to individual and combined effects range from -28.6 to 16.9 W m -2 and -29.7 to 12.2 W m -2 , respectively. This study advances understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offers guidance for improving snow simulations and RF estimates in Earth system models under climate change.

58 GEOSCIENCES↗