Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “variable selection”

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 1,081 records · Page 60

Within-host infectious disease models accommodating cellular coinfection, with an application to influenza

Within-host models are useful tools for understanding the processes regulating viral load dynamics. While existing models have considered a wide range of within-host processes, at their core these models have shown remarkable structural similarity. Specifically, the structure of these models generally consider target cells to be either uninfected or infected, with the possibility of accommodating further resolution (e.g. cells that are in an eclipse phase). Recent findings, however, indicate that cellular coinfection is the norm rather than the exception for many viral infectious diseases, and that cells with high multiplicity of infection are present over at least some duration of an infection. The reality of these cellular coinfection dynamics is not accommodated in current within-host models although it may be critical for understanding within-host dynamics. This is particularly the case if multiplicity of infection impacts infected cell phenotypes such as their death rate and their viral production rates. Here, we present a new class of within-host disease models that allow for cellular coinfection in a scalable manner by retaining the low-dimensionality that is a desirable feature of many current within-host models. The models we propose adopt the general structure of epidemiological ‘macroparasite’ models that allow hosts to be variably infected by parasites such as nematodes and host phenotypes to flexibly depend on parasite burden. Specifically, our within-host models consider target cells as ‘hosts’ and viral particles as ‘macroparasites’, and allow viral output and infected cell lifespans, among other phenotypes, to depend on a cell’s multiplicity of infection. We show with an application to influenza that these models can be statistically fit to viral load and other within-host data, and demonstrate using model selection approaches that they have the ability to outperform traditional within-host viral dynamic models. Important in vivo quantities such as the mean multiplicity of cellular infection and time-evolving reassortant frequencies can also be quantified in a straightforward manner once these macroparasite models have been parameterized. The within-host model structure we develop here provides a mathematical way forward to address questions related to the roles of cellular coinfection, collective viral interactions, and viral complementation in within-host viral dynamics and evolution.

59 BASIC BIOLOGICAL SCIENCES↗

Geochemical constraints on bacteriophage infectivity in terrestrial environments

Abstract Lytic phages can be potent and selective inhibitors of microbial growth and can have profound impacts on microbiome composition and function. However, there is uncertainty about the biogeochemical conditions under which phage predation modulates microbial ecosystem function, particularly in terrestrial systems. Ionic strength is critical for infection of bacteria by many phages, but quantitative data is limited on the ion thresholds for phage infection that can be compared with environmental ion concentrations. Similarly, while carbon composition varies in the environment, we do not know how this variability influences the impact of phage predation on microbiome function. Here, we measured the half-maximal effective concentrations (EC50) of 80 different inorganic ions for the infection of E. coli with two canonical dsDNA and ssRNA phages, T4 and MS2, respectively. Many alkaline earth metals and alkali metals enabled lytic infection but the ionic strength thresholds varied for different ions between phages. Additionally, using a freshwater nitrate-reducing microbiome, we found that the ability of lytic phages to influence nitrate reduction end-products depended upon the carbon source as well as ionic strength. For all phage:host pairs, the ion EC50s for phage infection exceeded the ion concentrations found in many terrestrial freshwater systems. Thus, our findings support a model where phages most influence terrestrial microbial functional ecology in hot spots and hot moments such as metazoan guts, drought influenced soils, or biofilms where ion concentration is locally or transiently elevated and nutrients are available to support the growth of specific phage hosts.

Carlson, Hans K. (ORCID:0000000215835313)↗

Identification of stress biomarkers for drought and increased soil temperature in seedlings of European beech ( Fagus sylvatica )

Drought is an environmental stress that impacts plant productivity. Projections show both an increase in intense rain events and a reduction in the number of rain days, conditions that leads to increased risk of drought. Consequently, the identification of molecular biomarkers suitable for evaluating the impact of water deprivation conditions on forest plant seedlings is of significant value for monitoring purposes and forest management. In this study, we evaluated a biochemical methodology for the assessment of drought stress coupled with variable soil temperature in European beech (Fagus sylvatica L.) seedlings by analyzing a set of metabolites and enzymes involved in free radical scavenging and cell wall synthesis. The results indicate that the specific activities and isoform profile of superoxide dismutases and glutathione peroxidases together with the variation of phenolic compounds enable discrimination between seedlings with different degrees of photosynthetic activity. This approach represents a promising platform for the assessment of drought stress in forest trees and could serve for enhancing selection and breeding practices, allowing for plants that are more tolerant of abiotic stress.

beach↗

time-resolved spectroscopy fit (trspecfit) v0.01

Analyze 2D time- and energy-resolved data, such as from a pump-probe spectroscopy experiment. User can select and input different peak shapes/ functions and background types to first fit a ground state/ unperturbed spectrum. This would be similar to how standard spectroscopy data is fit. Subsequently, to describe the time domain, users can choose functions that describe the temporal dynamics of one or more spectral features, such as a peak amplitude, peak position, etc. These time dynamics functions can be added or convoluted (e.g. describing an instrument response function) with each other. Functionality to integrate implicit variables leading to distributions of certain parameters/ functions is in development. Alternatively, 2D data can be analyzed one time step at a time to get an idea of the time dynamics of the system before deploying the global 2D fit described above. Typically people write custom software for this purpose. During my PhD I've seen five internal LBL and external researchers write one-off code in different languages to analyze time- and energy-resolved spectra. While this was specifically was for a laser pump - X-ray probe spectroscopy experiment, I'm trying to write a general package for the time-resolved spectroscopy community.

Mahl, Johannes [Lawrence Berkeley National Laborat↗

Group Projected subspace pursuit for IDENTification of variable coefficient differential equations (GP-IDENT)

We propose an effective and robust algorithm for identifying partial differential equations (PDEs) with space-time varying coefficients from the noisy observation of a single solution trajectory. Identifying unknown differential equations from noisy data is a difficult task, and it is even more challenging with space and time varying coefficients in the PDE. The proposed algorithm, GP-IDENT, has three ingredients: (i) we use B-spline bases to express the unknown space and time varying coefficients, (ii) we propose Group Projected Subspace Pursuit (GPSP) to find a sequence of candidate PDEs with various levels of complexity, and (iii) we propose a new criterion for model selection using the Reduction in Residual (RR) to choose an optimal one among a pool of candidates. The new GPSP considers group projected subspaces which is more robust than existing methods in distinguishing correlated group features. We test GP-IDENT on a variety of PDEs and PDE systems, and compare it with the state-of-the-art parametric PDE identification algorithms under different settings to illustrate its outstanding performance. Furthermore, our experiments show that GP-IDENT is effective in identifying the correct terms from a large dictionary, and our model selection scheme is robust to noise.

Data-driven method↗

Polarization and variability of compact sources measured in Planck time-ordered data

This paper introduces a new Planck Catalog of Polarized and Variable Compact Sources (PCCS-PV) comprising 153 sources, the majority of which are extragalactic. The data include both the total flux density and linear polarization measured by Planck with frequency coverage from 30 to 353 GHz, and temporal spacing ranging from days to years. We classify most sources as beamed, extragalactic radio sources; the catalog also includes several radio galaxies, Seyfert galaxies, and Galactic and Magellanic Cloud sources, including H II regions and planetary nebulae. An advanced extraction method applied directly to the multifrequency Planck time-ordered data, rather than the mission sky maps, was developed to allow an assessment of the variability of polarized sources. Our analysis of the time-ordered data from the Planck mission, tod2flux, allowed us to catalog the time-varying emission and polarization properties for these sources at the full range of polarized frequencies employed by Planck, 30-353 GHz. PCCS-PV provides the time-and frequency-dependent, polarized flux densities for all 153 sources. To illustrate some potential applications of the PCCS-PV, we conducted preliminary comparisons of our measurements of selected sources with published data from other astronomical instruments. In summary, we find general agreement between the Planck and the Institut de Radioastronomie Millimétrique (IRAM) polarization measurements as well as with the Metsähovi 37 GHz values at closely similar epochs. Here, these combined measurements also show the value of PCCS-PV results and the PCCS2 catalog for filling in missing spectral (or temporal) coverage and helping to define the spectral energy distributions of extragalactic sources. In turn, these results provide useful clues as to the physical properties of the sources.

79 ASTRONOMY AND ASTROPHYSICS↗

A Novel Protection Scheme for Unbalanced Faults in Inverter Dominated Networks: A Computationally Efficient Algorithm for Entry-Level Relays

Microgrids are now a common practice in distribution systems to increase resilience and reliability. However, microgrid protection remains a critical challenge, considering its requirement to operate in both grid connected and islanded, and the variability in fault characteristics under each mode of operation. This paper presents unbalanced power (S unb ) based fault detection algorithm, which considers local voltage and current unbalances to determine faults in the system. S unb is a computationally efficient fault detection algorithm that is suitable for implementation in the programmable logic of entry level protective relays. In addition, the difference in current and voltage unbalance (D n ) is used to determine the fault type. The proposed method demonstrates high sensitivity and selectivity for line-to-ground (LG), line-to-line (LL), and double line-to-ground (LLG) faults, representing the most common faults in distribution systems. It also allows relay coordination with upstream and downstream protection devices in both island and grid connected operation, while preserving grading margins. The same pickup and time multiplier settings of a particular relay for both modes of operation eliminates the need for adaptive settings, which rely on communication networks. Validation was performed with a hardware-in-the-loop (HIL) setup using Typhoon HIL real time simulator interfaced with three entry-level, SEL 751 relays. Results confirmed the algorithm’s ability to discriminate fault conditions, and determine the fault type under both operating modes, maintain fast detection times, and ensure proper protection coordination.

fault classification↗

Viral Envelope Evolution in Simian–HIV-Infected Neonate and Adult-Dam Pairs of Rhesus Macaques

We recently demonstrated that Simian–HIV (SHIV)-infected neonate rhesus macaques (RMs) generated heterologous HIV-1 neutralizing antibodies (NAbs) with broadly-NAb (bNAb) characteristics at a higher frequency compared with their corresponding dam. Here, we characterized genetic diversity in Env sequences from four neonate or adult/dam RM pairs: in two pairs, neonate and dam RMs made heterologous HIV-1 NAbs; in one pair, neither the neonate nor the dam made heterologous HIV-1 NAbs; and in another pair, only the neonate made heterologous HIV-1 NAbs. Phylogenetic and sequence diversity analyses of longitudinal Envs revealed that a higher genetic diversity, within the host and away from the infecting SHIV strain, was correlated with heterologous HIV-1 NAb development. We identified 22 Env variable sites, of which 9 were associated with heterologous HIV-1 NAb development; 3/9 sites had mutations previously linked to HIV-1 Env bNAb development. These data suggested that viral diversity drives heterologous HIV-1 NAb development, and the faster accumulation of viral diversity in neonate RMs may be a potential mechanism underlying bNAb induction in pediatric populations. Moreover, these data may inform candidate Env immunogens to guide precursor B cells to bNAb status via vaccination by the Env-based selection of bNAb lineage members with the appropriate mutations associated with neutralization breadth.

60 APPLIED LIFE SCIENCES↗

Regional Real-Time PV Spinning Reserve Estimator

Curtailed photovoltaic (PV) generation is a zero-marginal-cost spinning reserve that can be used for a number of active power control services. Unlike traditional spinning reserve providers, however, i.e., fossil-fueled generators, which have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate knowledge of the PV PHL is essential. It ensures that enough headroom is reserved by the PV plants to deliver the award services in real time and informs feasible dispatch decisions made by the market operator. To tackle this challenge, a novel reference-control grouping-based PV plant reserve estimation method has been proposed by the National Renewable Energy Laboratory under past projects funded by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office. The estimation method separates inverters within a plant into two groups: a control group and a reference group. While the reference group is reserved to operate at its PHL, the control group can be curtailed to provide the grid services. Real-time outputs from the reference inverters are used to estimate the PHL for the whole plant based on the ratio between capacities of the reference group and of the plant. This work further enhances the methodology by (1) improving the model accuracy through machine learning; (2) automating the reference inverter selection through correlation analysis; (3) considering estimation look-ahead windows; and (4) applying to regional spinning reserve estimation. Significant performance improvement has been observed based on real-world data collected by CAISO, Southern Company, and Terabase Energy. Compared with the original scaling method, the newly proposed machine learning-based approach reduces the estimation errors by 30% and 13% at the plant level and region level, respectively. Results obtained from this project are intended to be used by grid operators, market operators, balancing authorities, and PV plant owners and operators to facilitate PV participation in ancillary service markets. Regulators, policymakers, and system planners can also consider the results of this work in their decision-making processes. In addition to the performance improvement on the existing reference-control based grouping method, we also investigated how the variability of PV generation from a single PV inverter can be used to represent the variability of PV generation at the plant level.

24 POWER TRANSMISSION AND DISTRIBUTION↗

CheKiPEUQ Intro 2: Harnessing Uncertainties from Data Sets, Bayesian Design of Experiments in Chemical Kinetics**

When choosing experimental conditions, Bayesian statistical tools can predict the experimental choices which will yield the highest information gain. Experimental choices could be temperature, pressure, reaction time, number of measurements, reactor volume, etc.. Three example analyses are presented here, each using the software Chemical Kinetics Parameter Estimation and Uncertainty Quantification (CheKiPEUQ). Information gain is a measure of reduction of uncertainty in a model's parameters. The three chemical system examples presented each illustrate Bayesian Design of Experiments using information gain. In the first chemical example, temperature selection impacts the information gain for the free energy of reaction in a two-component equilibrium reaction. In the second example, temperature and pressure are explored for a competitive adsorption Langmuir replacement reaction system. Finally, the third example is a catalytic membrane reactor which is a culmination of the previous examples. The catalytic membrane reactor has a complex and nonlinear response in the observables which is solved by numerical evaluation. In the three examples, the experimental conditions are treated as design variables for maximizing information gain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores

Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid–wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R 2 ) of ~0.99 and a mean squared error of ~2.9 × 10 –5 . Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Modular Linear Thermoviscoelastic Model

Time-dependent, viscoelastic responses of materials like polymers and glasses have long been studied. As such, a variety of models have been put forth to describe the behavior including simple rheological models (e.g. Maxwell, Kelvin), linear "fading memory" theories, and hereditary integral based linear thermal viscoelastic approaches as well as more recent nonlinear theories that are either integral, fictive temperature, or differential internal state variable based. The current work details a new LINEAR_THERMOVISCOELASTIC model that has been added to LAME. This formulation represents a viscoelastic theory that neglects some of the phenomenological details of the PEC/SPEC models in favor of efficiency and simplicity. Furthermore, this new model is a first step towards developing "modular" viscoelastic capabilities akin to those available with hardening descriptions for plasticity models in LAME. Specifically, multiple different (including user-defined) shift-factor forms are implemented with each being easily selected via parameter specification rather than requiring distinct material models.

36 MATERIALS SCIENCE↗

Urban weather modeling using WRF: linking physical assumptions, code implementation, and observational needs

The Weather Research and Forecasting (WRF) model includes urban schemes that simulate the influence of urban surfaces on the atmosphere using parameterizations for flux, and radiative exchanges. Three core schemes – the Bulk urban parameterization, Single-Layer Urban Canopy Model (SLUCM), and Multi-Layer Urban Canopy Model (MLUCM) – represent increasing levels of complexity. Although the parameterizations within these urban schemes are described in the literature, their specific implementation remains poorly documented, thus slowing down model development efforts. This manuscript provides a roadmap to the three urban schemes in WRF version 4.5.2, presenting equations using the same symbols as in the model code, along with references to code lines, and including graphics and explanations that connect the code to its physical foundations. Our thorough review of the urban parameterizations implemented in WRF version 4.5.2 highlighted a handful of parameters that may introduce discontinuities in simulations: (i) in the SLUCM, a 1 mm h −1 rain rate threshold is employed to switch between two minimum moisture availability parameterizations, thus impacting latent heat flux calculations; (ii) in the SLUCM a threshold is used to partition shortwave radiation into direct and diffuse components; (iii) in all three urban schemes, the bulk Richardson number is employed to select the similarity function, which influences the vertical distribution of heat and momentum. We also identified a highly simplified treatment of the radiative balance on roof surfaces. The implications of these simplifications can be assessed through targeted observations across relevant conditions, including varying precipitation rates, cloud cover, and transitions between stability regimes. Furthermore, the widespread application of the Monin-Obukhov similarity theory in these urban schemes warrants model evaluation under highly stable and unstable conditions and in heterogeneous urban settings with variable land cover and building heights on scales finer than model resolution. To address these challenges, we offer guidance on observational strategies, emphasizing the need for multi-parametric measurements to capture potential compensating biases and multi-height measurements that align with the levels where quantities are diagnostic and prognosed in the model (i.e., the lowest atmospheric level of the WRF model). Finally, our inspection of the code revealed implementation bugs that have now been corrected in WRF versions 4.6.0 and 4.6.1. Sensitivity tests over the Atlanta urban area show that these corrections affect surface temperatures, underscoring the importance of performing rigorous documentation and verification of the implementation of parameterizations in model code.

54 ENVIRONMENTAL SCIENCES↗

Structural Comparison of Human Anti-HIV-1 gp120 V3 Monoclonal Antibodies of the Same Gene Usage Induced by Vaccination and Chronic Infection

ABSTRACT Elucidating the structural basis of antibody (Ab) gene usage and affinity maturation of vaccine-induced Abs can inform the design of immunogens for inducing desired Ab responses in HIV vaccine development. Analyses of monoclonal Abs (MAbs) encoded by the same immunoglobulin genes at different stages of maturation can help to elucidate the maturation process. We have analyzed four human anti-V3 MAbs with the same VH1-3*01 and VL3-10*01 gene usage. Two MAbs, TA6 and TA7, were developed from a vaccinee in the HIV vaccine phase I trial DP6-001 with a polyvalent DNA prime/protein boost regimen, and two others, 311-11D and 1334, were developed from HIV-infected patients. The somatic hypermutation (SHM) rates in VH of vaccine-induced MAbs are lower than in chronic HIV infection-induced MAbs, while those in VL are comparable. Crystal structures of the antigen-binding fragments (Fabs) in complex with V3 peptides show that these MAbs bind the V3 epitope with a new cradle-binding mode and that the V3 β-hairpin lies along the antigen-binding groove, which consists of residues from both heavy and light chains. Residues conserved from the germ line sequences form specific binding pockets accommodating conserved structural elements of the V3 crown hairpin, predetermining the Ab gene selection, while somatically mutated residues create additional hydrogen bonds, electrostatic interactions, and van der Waals contacts, correlating with an increased binding affinity. Our data provide a unique example of germ line sequences determining the primordial antigen-binding sites and SHMs correlating with affinity maturation of Abs induced by vaccine and natural HIV infection. IMPORTANCE Understanding the structural basis of gene usage and affinity maturation for anti-HIV-1 antibodies may help vaccine design and development. Antibodies targeting the highly immunogenic third variable loop (V3) of HIV-1 gp120 provide a unique opportunity for detailed structural investigations. By comparing the sequences and structures of four anti-V3 MAbs at different stages of affinity maturation but of the same V gene usage, two induced by vaccination and another two by chronic infection, we provide a fine example of how germ line sequence determines the essential elements for epitope recognition and how affinity maturation improves the antibody's recognition of its epitope.

36 MATERIALS SCIENCE↗

Rapid Optimization of Total Variation with Applications in Imaging, Additive Manufacturing, and Qualification

Total Variation optimization penalizes the gradient of a control variable or state. While this work focuses on image processing in particular, it has also found applications in inverse problems and topology optimization. In image processing, the goal is to maintain faithfulness to the original image while denoising and/or deblurring. Additionally, bilevel optimization over the spatially varying regularization weights can illuminate interfaces such as damage regions and other anomalies. We will address two fundamental challenges with TV-optimization: (i) the typical slow convergence of existing TV-optimization methods, and (ii) the selection of spatially varying TV parameters to promote interface detection. Additionally, we will apply such techniques to image data collected in additive manufacturing. In said context, stochasticity in build events induces flaws in the manufactured piece, compromising the integrity of said part. There is a critical need for in-situ monitoring to spot anomalies once they form, and in this setting we apply our total variation and hyperparameter solvers. We will develop a customized algorithm based on for extreme-scale TV-optimization that achieves super-linear or quadratic-convergence, a critical property for real-time, image-by-image analysis. A worst-case outcome is a preprocessing step that enhances image quality in-situ, specifically for out-of-focus and noisy images.

36 MATERIALS SCIENCE↗

Wind Plant Performance Prediction (WP3) Project

The methods for analysis of operational wind plant data are highly variable across the wind industry, leading to high uncertainties in the validation and bias-correction of preconstruction energy estimation methods. Lack of credibility in the preconstruction energy estimates leads to significant impacts on project financing and therefore the final levelized cost of energy for the plant. In this work, the variation in the evaluation of a wind plant's operational energy production as a result of variations in the processing methods applied to the operational data is examined. Preliminary results indicate that selection of the filters applied to the data and the filter parameters can have significant impacts in the final computed assessment metrics.

17 WIND ENERGY↗

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗

A Framework for Closed-Loop Optimization of an Automated Mechanical Serial-Sectioning System via Run-to-Run Control as Applied to a Robo-Met.3D

Optimization of automated data collection is gaining increased interest for the purposes of enabling closed-loop self-correcting systems that inherently maximize operational efficiencies and reduce waste. Many data collection systems have several variables which influence data accuracy or consistency and which can require frequent user interaction to be monitored and maintained. Operating upon a Robo-MET.3D™ automated mechanical serial-sectioning system, a run-to-run control algorithm has been developed to accelerate data collection and reduce data inconsistency. Here, using historical data amassed over a decade of experiments, a linear regression model of the deterministic system dynamics is created and used to employ a run-to-run control algorithm that optimizes selected system inputs to reduce operator intervention and increase efficacy while reducing variance of system output.

42 ENGINEERING↗