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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Mean-velocity profile of smooth channel flow explained by a cospectral budget model with wall-blockage

A series of recent studies has shown that a model of the turbulent vertical velocity variance spectrum (Fvv) combined with a simplified cospectral budget can reproduce many macroscopic flow properties of turbulent wall-bounded flows, including various features of the mean-velocity profile (MVP), i.e., the “law of the wall”. While the approach reasonably models the MVP’s logarithmic layer, the buffer layer displays insufficient curvature compared to measurements. The assumptions are re-examined here using a direct numerical simulation (DNS) dataset at moderate Reynolds number that includes all the requisite spectral and co-spectral information. Starting with several hypotheses for the cause of the “missing” curvature in the buffer layer, it is shown that the curvature deficit is mainly due to mismatches between (i) the modelled and DNS-observed pressure-strain terms in the cospectral budget and (ii) the DNS-observed Fvv and the idealized form used in previous models. By replacing the current parameterization for the pressure-strain term with an expansive version that directly accounts for wall-blocking effects, the modelled and DNS reported pressure-strain profiles match each other in the buffer and logarithmic layers. Forcing the new model with DNS-reported Fvv rather than the idealized form previously used reproduces the missing buffer layer curvature to high fidelity thereby confirming the “spectral link” between Fvv and the MVP across the full profile. A broad implication of this work is that much of the macroscopic properties of the flow (such as the MVP) may be derived from the energy distribution in turbulent eddies (i.e., Fvv) representing the microstate of the flow, provided the link between them accounts for wall-blocking.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Probing reionization with the cross-power spectrum of 21 cm and near-infrared radiation backgrounds

The cross-correlation between the 21 cm emission from the high-redshift intergalactic medium and the near-infrared (NIR) background light from high-redshift galaxies promises to be a powerful probe of cosmic reionization. In this paper, we investigate the cross-power spectrum during the epoch of reionization. We employ an improved halo approach to derive the distribution of the density field and consider two stellar populations in the star formation model: metal-free stars and metal-poor stars. The reionization history is further generated to be consistent with the electron-scattering optical depth from cosmic microwave background measurements. Then, the intensity of the NIR background is estimated by collecting emission from stars in first-light galaxies. On large scales, we find that the 21 cm and NIR radiation backgrounds are positively correlated during the very early stages of reionization. However, these two radiation backgrounds quickly become anti-correlated as reionization proceeds. The maximum absolute value of the cross-power spectrum is |Δ{sub 21,NIR}{sup 2}|∼10{sup −4} mK nW m{sup –2} sr{sup –1}, reached at ℓ ∼ 1000 when the mean fraction of ionized hydrogen is x-bar{sub i}∼0.9. We find that Square Kilometer Array can measure the 21 cm-NIR cross-power spectrum in conjunction with mild extensions to the existing CIBER survey, provided that the integration time independently adds up to 1000 and 1 hr for 21 cm and NIR observations, and that the sky coverage fraction of the CIBER survey is extended from 4 × 10{sup –4} to 0.1. Measuring the cross-correlation signal as a function of redshift provides valuable information on reionization and helps confirm the origin of the 'missing' NIR background.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

On the discovery of stars, quasars, and galaxies in the Southern Hemisphere with S-PLUS DR2

ABSTRACT This paper provides a catalogue of stars, quasars, and galaxies for the Southern Photometric Local Universe Survey Data Release 2 (S-PLUS DR2) in the Stripe 82 region. We show that a 12-band filter system (5 Sloan-like and 7 narrow bands) allows better performance for object classification than the usual analysis based solely on broad bands (regardless of infrared information). Moreover, we show that our classification is robust against missing values. Using spectroscopically confirmed sources retrieved from the Sloan Digital Sky Survey DR16 and DR14Q, we train a random forest classifier with the 12 S-PLUS magnitudes + 4 morphological features. A second random forest classifier is trained with the addition of the W1 (3.4 $\mu\mathrm{m} $) and W2 (4.6 $\mu\mathrm{m} $) magnitudes from the Wide-field Infrared Survey Explorer (WISE). Forty-four per cent of our catalogue have WISE counterparts and are provided with classification from both models. We achieve 95.76 per cent (52.47 per cent) of quasar purity, 95.88 per cent (92.24 per cent) of quasar completeness, 99.44 per cent (98.17 per cent) of star purity, 98.22 per cent (78.56 per cent) of star completeness, 98.04 per cent (81.39 per cent) of galaxy purity, and 98.8 per cent (85.37 per cent) of galaxy completeness for the first (second) classifier, for which the metrics were calculated on objects with (without) WISE counterpart. A total of 2926 787 objects that are not in our spectroscopic sample were labelled, obtaining 335 956 quasars, 1347 340 stars, and 1243 391 galaxies. From those, 7.4 per cent, 76.0 per cent, and 58.4 per cent were classified with probabilities above 80 per cent. The catalogue with classification and probabilities for Stripe 82 S-PLUS DR2 is available for download.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Exploration of Noncoding Sequences in Metagenomes

Environment-dependent genomic features have been defined for different metagenomes, whose genes and their associated processes are related to specific environments. Identification of ORFs and their functional categories are the most common methods for association between functional and environmental features. However, this analysis based on finding ORFs misses noncoding sequences and, therefore, some metagenome regulatory or structural information could be discarded. In this work we analyzed 23 whole metagenomes, including coding and noncoding sequences using the following sequence patterns: (G+C) content, Codon Usage (Cd), Trinucleotide Usage (Tn), and functional assignments for ORF prediction. Herein, we present evidence of a high proportion of noncoding sequences discarded in common similarity based methods in metagenomics, and the kind of relevant information present in those. We found a high density of trinucleotide repeat sequences (TRS) in noncoding sequences, with a regulatory and adaptive function for metagenome communities. We present associations between trinucleotide values and gene function, where metagenome clustering correlate with microorganism adaptations and kinds of metagenomes. We propose here that noncoding sequences have relevant information to describe metagenomes that could be considered in a whole metagenome analysis in order to improve their organization, classification protocols, and their relation with the environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Sequential Projection Pursuit Principal Component Analysis – Dealing with Missing Data Associated with New -Omics Technologies

We present a new version of sequential projection pursuit Principal Component Analysis (sppPCA) that has the capability to perform PCA on large multivariate datasets that contain non-random missing values. We demonstrate that sppPCA generates more robust and informative low-dimensional representations of the data than imputation-based approaches and improved downstream statistical analyses, such as clustering or classification. A Java program to run sppPCA is freely available at https://www.biopilot.org/docs/Software/sppPCA.

Webb-Robertson, Bobbie-Jo M.↗
DOE OSTI2022

Jobs, jobs, jobs: what’s an analyst to do?

Analysts and economists often face the task of using employment metrics to characterize industries of interest. Some key challenges can be understanding where to find employment metrics, the differences in various employment metrics, and when each metric should be used. This article analyzes a variety of publicly available employment data for the United States and compares these data. A detailed description of the intricacies of each data source is provided, which covers factors such as regionality, industry breakout, periodicity, and the types of jobs included. This article provides several case study examples, using the oil and gas extraction, coal mining, and chemical manufacturing sectors to portray challenges data users may face when developing employment estimates that suit their needs. Data users should be aware of a variety of data sources to understand alternative analysis options when data limitations are present and to determine which data source best meets their needs. Instances may occur in which information from one dataset may be used to help impute missing values.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2013

Radiative Forcing of the Direct Aerosol Effect from AeroCom Phase II Simulations

We report on the AeroCom Phase II direct aerosol effect (DAE) experiment where 16 detailed global aerosol models have been used to simulate the changes in the aerosol distribution over the industrial era. All 16 models have estimated the radiative forcing (RF) of the anthropogenic DAE, and have taken into account anthropogenic sulphate, black carbon (BC) and organic aerosols (OA) from fossil fuel, biofuel, and biomass burning emissions. In addition several models have simulated the DAE of anthropogenic nitrate and anthropogenic influenced secondary organic aerosols (SOA). The model simulated all-sky RF of the DAE from total anthropogenic aerosols has a range from −0.58 to −0.02 W m(sup−2), with a mean of −0.27 W m(sup−2 for the 16 models. Several models did not include nitrate or SOA and modifying the estimate by accounting for this with information slightly strengthens the mean. Modifying the model estimates for missing aerosol components and for the time period 1750 to 2010 results in a mean RF for the DAE of −0.35 W m(sup−2). Compared to AeroCom Phase I (Schulz et al., 2006) we find very similar spreads in both total DAE and aerosol component RF. However, the RF of the total DAE is stronger negative and RF from BC from fossil fuel and biofuel emissions are stronger positive in the present study than in the previous AeroCom study.We find a tendency for models having a strong (positive) BC RF to also have strong (negative) sulphate or OA RF. This relationship leads to smaller uncertainty in the total RF of the DAE compared to the RF of the sum of the individual aerosol components. The spread in results for the individual aerosol components is substantial, and can be divided into diversities in burden, mass extinction coefficient (MEC), and normalized RF with respect to AOD. We find that these three factors give similar contributions to the spread in results

Computer simulation↗
NASA NTRS2017

Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas

Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) can monitor power outages in disaster-affected areas through the identification of missing city lights. When combined with locally-relevant geospatial information, these observations can be used to estimate power outages, defined as geographic locations requiring manual intervention to restore power. In this study, we produced a power outage product based on Suomi-NPP VIIRS DNB observations to estimate power outages following Hurricane Sandy in 2012. This product, combined with known power outage data and ambient population estimates, was then used to predict power outages in a layered, feedforward neural network model. We believe this is the first attempt to synergistically combine such data sources to quantitatively estimate power outages. The VIIRS DNB power outage product was able to identify initial loss of light following Hurricane Sandy, as well as the gradual restoration of electrical power. The neural network model predicted power outages with reasonable spatial accuracy, achieving Pearson coefficients (r) between 0.48 and 0.58 across all folds. Our results show promise for producing a continental United States (CONUS)- or global-scale power outage monitoring network using satellite imagery and locally-relevant geospatial data.

da/night band↗
DOE OSTI2011

Measurement of the $W$ Boson Mass and Width at the D0 Experiment

I present a precise measurement of W boson mass measurement in electron decay channel using data collected by the D0 detector at the Fermilab Tevatron Collider. A binned likelihood fit method is used to extract the mass information from the transverse mass, the electron transverse momentum and missing transverse energy distributions. I also present a precise direct measurement ofWboson width using the events with large transverse mass. The W mass result can be used to put stringent indirect limits on the Standard Model Higgs boson mass.

43 PARTICLE ACCELERATORS↗
NASA NTRS2018

CA Risk Assessment Approaches

Current single-Pc approach work well when subtended by appropriate hypothesis-testing framework. Reframing construct in terms of miss distance distribution would confer certain advantages such as reducing to same import as Pc, but still convey some additional information. Methods to incorporate known uncertainties in covariances and HBR should be pursued that can also be framed in terms of miss distance distribution. We present Plausibilistic II and Possibilistic constructs, to determine their adequacy and viable recommendation.

Conjunction↗
NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗
DOE OSTI2014

Handling Large and Complex Data in a Photovoltaic Research Institution Using a Custom Laboratory Information Management System

Twenty-five years ago the desktop computer started becoming ubiquitous in the scientific lab. Researchers were delighted with its ability to both control instrumentation and acquire data on a single system, but they were not completely satisfied. There were often gaps in knowledge that they thought might be gained if they just had more data and they could get the data faster. Computer technology has evolved in keeping with Moore’s Law meeting those desires; however those improvements have of late become both a boon and bane for researchers. Computers are now capable of producing high speed data streams containing terabytes of information; capabilities that evolved faster than envisioned last century. Software to handle large scientific data sets has not kept up. How much information might be lost through accidental mismanagement or how many discoveries are missed through data overload are now vital questions. An important new task in most scientific disciplines involves developing methods to address those issues and to create the software that can handle large data sets with an eye towards scalability. This software must create archived, indexed, and searchable data from heterogeneous instrumentation for the implementation of a strong data-driven materials development strategy. At the National Center for Photovoltaics in the National Renewable Energy Laboratory, we began development a few years ago on a Laboratory Information Management System (LIMS) designed to handle lab-wide scientific data acquisition, management, processing and mining needs for physics and materials science data, and with a specific focus towards future scalability for new equipment or research focuses. We will present the decisions, processes, and problems we went through while building our LIMS system for materials research, its current operational state and our steps for future development.

14 SOLAR ENERGY↗
NASA NTRS2012

Correction to "Influence of Dust and Black Carbon on the Snow Albedo in the NASA Goddard Earth Observing System Version 5 Land Surface Model"

The website information describing the forcing meteorological data used for the land surface model (LSM) simulation, which were observed at an Automated Meteorological Station CAWS) at the Sapporo District Meteorological Observatory maintained by the Japan Meteorological Agency (JMA), was missing from the text. The 1-hourly data were obtained from the website of Kisyoutoukeijouhou (Information for available JMA-observed meteorological data in the past) on the website of JMA (in Japanese) (available at: http://www.jma.go.jpijmaimenulreport.html). The measurement height information of 59.5 m for the anemometer at the Sapporo Observatory was also obtained from the website of JMA (in Japanese) (available at: http://www.jma.go.jp/jma/menu/report.html). In addition, the converted 10-m wind speed, based on the AWS/JMA data, was further converted to a 2-m wind speed prior to its use with the land model as a usual treatment of off-line Catchment simulation. Please ignore the ice absorption data on the website mentioned in paragraph [15] which was not used for our calculations (but the data on the website was mostly the same as the estimated ice absorption coefficients by the following method because they partially used the same data by Warren [1984]). We calculated the ice absorption coefficients with the method mentioned in the same paragraph, for which some of the refractive index data by Warren [1984] were used and then interpolated between wavelengths, and also mentioned in paragraph [20] for the visible (VIS) and near-infrared (NIR) ranges. The optical data we used were interpolated between wavelengths as necessary.

Yasunari, Teppei J.↗
NASA NTRS2007

System for Secure Integration of Aviation Data

The Aviation Data Integration System (ADIS) of Ames Research Center has been established to promote analysis of aviation data by airlines and other interested users for purposes of enhancing the quality (especially safety) of flight operations. The ADIS is a system of computer hardware and software for collecting, integrating, and disseminating aviation data pertaining to flights and specified flight events that involve one or more airline(s). The ADIS is secure in the sense that care is taken to ensure the integrity of sources of collected data and to verify the authorizations of requesters to receive data. Most importantly, the ADIS removes a disincentive to collection and exchange of useful data by providing for automatic removal of information that could be used to identify specific flights and crewmembers. Such information, denoted sensitive information, includes flight data (here signifying data collected by sensors aboard an aircraft during flight), weather data for a specified route on a specified date, date and time, and any other information traceable to a specific flight. The removal of information that could be used to perform such tracing is called "deidentification." Airlines are often reluctant to keep flight data in identifiable form because of concerns about loss of anonymity. Hence, one of the things needed to promote retention and analysis of aviation data is an automated means of de-identification of archived flight data to enable integration of flight data with non-flight aviation data while preserving anonymity. Preferably, such an automated means would enable end users of the data to continue to use pre-existing data-analysis software to identify anomalies in flight data without identifying a specific anomalous flight. It would then also be possible to perform statistical analyses of integrated data. These needs are satisfied by the ADIS, which enables an end user to request aviation data associated with de-identified flight data. The ADIS includes client software integrated with other software running on flight-operations quality-assurance (FOQA) computers for purposes of analyzing data to study specified types of events or exceedences (departures of flight parameters from normal ranges). In addition to ADIS client software, ADIS includes server hardware and software that provide services to the ADIS clients via the Internet (see figure). The ADIS server receives and integrates flight and non-flight data pertaining to flights from multiple sources. The server accepts data updates from authorized sources only and responds to requests from authorized users only. In order to satisfy security requirements established by the airlines, (1) an ADIS client must not be accessible from the Internet by an unauthorized user and (2) non-flight data as airport terminal information system (ATIS) and weather data must be displayed without any identifying flight information. ADIS hardware and software architecture as well as encryption and data display scheme are designed to meet these requirements. When a user requests one or more selected aviation data characteristics associated with an event (e.g., a collision, near miss, equipment malfunction, or exceedence), the ADIS client augments the request with date and time information from encrypted files and submits the augmented request to the server. Once the user s authorization has been verified, the server returns the requested information in de-identified form.

Kulkarni, Deepak↗
DOE OSTI2023

Comparing Individualized Survival Predictions From Random Survival Forests and Multistate Models in the Presence of Missing Data: A Case Study of Patients With Oropharyngeal Cancer

Background: In recent years, interest in prognostic calculators for predicting patient health outcomes has grown with the popularity of personalized medicine. These calculators, which can inform treatment decisions, employ many different methods, each of which has advantages and disadvantages. Methods: We present a comparison of a multistate model (MSM) and a random survival forest (RSF) through a case study of prognostic predictions for patients with oropharyngeal squamous cell carcinoma. The MSM is highly structured and takes into account some aspects of the clinical context and knowledge about oropharyngeal cancer, while the RSF can be thought of as a black-box non-parametric approach. Key in this comparison are the high rate of missing values within these data and the different approaches used by the MSM and RSF to handle missingness. Results: We compare the accuracy (discrimination and calibration) of survival probabilities predicted by both approaches and use simulation studies to better understand how predictive accuracy is influenced by the approach to (1) handling missing data and (2) modeling structural/disease progression information present in the data. We conclude that both approaches have similar predictive accuracy, with a slight advantage going to the MSM. Conclusions: Although the MSM shows slightly better predictive ability than the RSF, consideration of other differences are key when selecting the best approach for addressing a specific research question. These key differences include the methods’ ability to incorporate domain knowledge, and their ability to handle missing data as well as their interpretability, and ease of implementation. Ultimately, selecting the statistical method that has the most potential to aid in clinical decisions requires thoughtful consideration of the specific goals.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN)-based deep learning models is a powerful technique to detect features from material micrographs in an automated manner. In contrast to microstructural classification, supervised CNN models for segmentation tasks require pixel-wise annotation labels. However, manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. Microstructural characterization especially needs to be expedited for faster material discovery by changing alloy compositions. Here, in this study, we attempt to overcome such limitations by utilizing multimodal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy images of 347H stainless steel as training data and electron backscatter diffraction micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. The viability of our method is evaluated by considering a set of deep CNN architectures. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that naïve pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Elucidation of Peptide-Directed Palladium Surface Structure for Biologically Tunable Nanocatalysts

Peptide-enabled synthesis of inorganic nanostructures represents an avenue to access catalytic materials with tunable and optimized properties. This is achieved via peptide complexity and programmability that is missing in traditional ligands for catalytic nanomaterials. Unfortunately, there is limited information available to correlate peptide sequence to particle structure and catalytic activity to date. As such, the application of peptide-enabled nanocatalysts remains limited to trial and error approaches. In this paper, a hybrid experimental and computational approach is introduced to systematically elucidate biomolecule-dependent structure/function relationships for peptide-capped Pd nanocatalysts. Synchrotron X-ray techniques were used to uncover substantial particle surface structural disorder, which was dependent upon the amino acid sequence of the peptide capping ligand. Nanocatalyst configurations were then determined directly from experimental data using reverse Monte Carlo methods and further refined using molecular dynamics simulation, obtaining thermodynamically stable peptide-Pd nanoparticle configurations. Sequence-dependent catalytic property differences for C-C coupling and olefin hydrogenation were then eluddated by identification of the catalytic active sites at the atomic level and quantitative prediction of relative reaction rates. This hybrid methodology provides a clear route to determine peptide-dependent structure/function relationships, enabling the generation of guidelines for catalyst design through rational tailoring of peptide sequences

atomic pair distribution function↗
DOE OSTI2014

Evaluation of Missed Energy Saving Opportunity Based on Illinois Home Performance Program Field Data: Homeowner Selected Upgrades Versus Cost-Optimized Solutions

Expanding on previous research by PARR, this study compares measure packages installed during 800 Illinois Home Performance with ENERGY STAR® (IHP) residential retrofits to those recommended as cost-optimal by Building Energy Optimization (BEopt™) modeling software. In previous research, cost-optimal measure packages were identified for fifteen Chicagoland single family housing archetypes, called housing groups. In the present study, 800 IHP homes are first matched to one of these fifteen housing groups, and then the average measures being installed in each housing group are modeled using BEopt to estimate energy savings. For most housing groups, the differences between recommended and installed measure packages is substantial. By comparing actual IHP retrofit measures to BEopt-recommended cost-optimal measures, missed savings opportunities are identified in some housing groups; also, valuable information is obtained regarding housing groups where IHP achieves greater savings than BEopt-modeled, cost-optimal recommendations. Additionally, a measure-level sensitivity analysis conducted for one housing group reveals which measures may be contributing the most to gas and electric savings. Overall, the study finds not only that for some housing groups, the average IHP retrofit results in more energy savings than would result from cost-optimal, BEopt recommended measure packages, but also that linking home categorization to standardized retrofit measure packages provides an opportunity to streamline the process for single family home energy retrofits and maximize both energy savings and cost-effectiveness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗