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At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dynamic Analysis of a Six-Cable Parallel Robot for Automated Panelized Building Retrofits

Cable-Driven Parallel Robots (CDPRs) are highly suitable for automated panelized building retrofits, thanks to their compact footprint and high payload-to-weight ratio. A common CDPR configuration featuring eight cables, where the anchors form a rectangular prism in front of the building facade, offers a large wrench feasible workspace and good control versatility. However, installing upper anchor points requires additional support structures, such as towers or beams, increasing setup complexity and posing logistical challenges in construction settings. To mitigate these challenges, we propose a six-cable CDPR model specifically designed for automated panelized building retrofits. Although the feasible workspace is limited, our analysis shows that the proposed CDPR adequately covers the critical areas required for panel installation. To validate that the six-cable system can effectively transport the end effector to the desired installation pose, we calculated optimal trajectories based on a constrained dynamic model. The simulation results of the six-cable CDPR demonstrate promising potential for automated panelized building retrofits, effectively balancing simplicity, cost-effectiveness, and functionality.

Liu, Yifang [ORNL] (ORCID:0000000190817417)↗
DOE OSTI2026

Automated tape-target centering based on template matching for construction measurement tasks using robotic total station

Robotic total stations have transformed surveying and construction mapping through precise, efficient, and automated measurements. These instruments integrate a theodolite, which measures horizontal and vertical angles, with an electronic distance measurement (EDM) unit to determine distances, allowing accurate 3D measurement of observable points. Traditionally, users manually aimed the total station at a target. Recent advancements in robotic motor control and integration of cameras now enable automatic rotation and aiming, typically requiring only a single operator to position the target. Automated aiming is commonly performed using retroreflector prisms, which reflect light back to the source with minimal scattering, enabling high-precision measurements over long distances. However, retroreflectors, especially those designed for 360 degree, can be expensive and impractical for certain applications. This paper presents an algorithm for automating the center detection of low-cost disposable tape targets using the total station's onboard camera and a template matching algorithm. The algorithm identifies the target's center pixel in the image, and the instrument is commanded to aim at that location. We evaluated the performance of this template-matching-based centering method under various distances, angles, lighting conditions, and field environments, comparing its results to both manual aiming and conventional automated aiming of tape targets. Manual aiming was used as the baseline operational reference in the absence of an independent ground-truth measurement. The proposed approach achieves target centering results comparable to the manual baseline. This method provides a cost-effective alternative for high-precision applications where retroreflector targets are constrained by budget or logistics.

Harrington, Joshua [ORNL]↗
DOE OSTI2016

Soil and water conservation using the socioeconomics, sustainability concerns, and policy preference for residual biomass harvest

While woody bioenergy opens up a new market opportunity for residual biomass, overharvesting of residual biomass can adversely affect soil health, water quality, timber health and stand productivity, among others. As such, it is important to understand the proportion of residual biomass forestland owners are willing to leave unharvested for soil nutrient and other environmental benefit purposes, their sustainability concerns, and policy preferences. Toward this end, we conducted a mail survey on 900 randomly selected forestland owners in Virginia and used ordinal logistic regression, cluster analyses, and Cochran-Armitage trend tests to analyze the data. Our analyses of the 121 most complete responses suggest that factors such as ownership tenure, mode of land acquisition, size of forestland, and forestland own- ership objectives, among other factors, affect decisions regarding the proportion of residual biomass forestland owners choose to leave unharvested. Analyses of landowners’ sustainability concerns indicate a clustering pattern, where concerns about sufficiency of best management practices and the potential implications of harvest decision on soil and water quality are among the statistical representatives of their respective clusters. Respondents likely to engage in harvest practices that do not leave any residual biomass on the ground have a preference for policies that help cover management cost. Decision makers administering or considering such policy proposals should be aware of the inadvertent effect such cost sharing arrange- ments can have in encouraging unsustainable practices.These results also hint at the potential need for combining such cost sharing arrangements with extension and outreach programs. Lastly, by accounting for landowners’ concerns and preferences, this study complements previous studies that primarily adopt engineering based approaches and solutions.

bioenergy↗
DOE OSTI2019

Data associated with "Methane emissions from gathering and boosting compressor stations in the U.S. Supporting volume 1: Multi-day measurements of pneumatic controller emissions"

This study was part of a larger study (the Gathering Emission Factor, or "GEF") study [1] to develop activity and methane emission factors for EPA's Greenhouse Gas Inventory (EPA GHGI) using direct emission measurements at the device level on all classes of equipment found on gathering compressor stations. To accomplish this, Colorado State University (CSU) partnered with the engineering firm AECOM to assist with planning, logistics, field work and analysis. Nine midstream natural gas companies – Anadarko Petroleum, Equinor, DCP Midstream, Kinder Morgan, MarkWest Energy Partners, Pioneer Natural Resources, Southwestern Energy, Williams Companies Inc., and XTO Energy Inc. – acted as partners in the study, provided site access for measurements, and provided representatives to advisory committees. At all times, and with the encouragement of industry partners, CSU maintained control the sampling plan, analysis and reporting. This study documents one part of the larger study - emissions from pneumatic actuated valve controllers (PC).

03 NATURAL GAS↗
DOE OSTI2021

Gulf of Mexico blue hole harbors high levels of novel microbial lineages

Exploration of oxygen-depleted marine environments has consistently revealed novel microbial taxa and metabolic capabilities that expand our understanding of microbial evolution and ecology. Marine blue holes are shallow karst formations characterized by low oxygen and high organic matter content. They are logistically challenging to sample, and thus our understanding of their biogeochemistry and microbial ecology is limited. We present a metagenomic and geochemical characterization of Amberjack Hole on the Florida continental shelf (Gulf of Mexico). Dissolved oxygen became depleted at the hole’s rim (32 m water depth), remained low but detectable in an intermediate hypoxic zone (40–75 m), and then increased to a secondary peak before falling below detection in the bottom layer (80–110 m), concomitant with increases in nutrients, dissolved iron, and a series of sequentially more reduced sulfur species. Microbial communities in the bottom layer contained heretofore undocumented levels of the recently discovered phylum Woesearchaeota (up to 58% of the community), along with lineages in the bacterial Candidate Phyla Radiation (CPR). Thirty-one high-quality metagenome-assembled genomes (MAGs) showed extensive biochemical capabilities for sulfur and nitrogen cycling, as well as for resisting and respiring arsenic. One uncharacterized gene associated with a CPR lineage differentiated hypoxic from anoxic zone communities. Overall, microbial communities and geochemical profiles were stable across two sampling dates in the spring and fall of 2019. The blue hole habitat is a natural marine laboratory that provides opportunities for sampling taxa with under-characterized but potentially important roles in redox-stratified microbial processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Greater sage-grouse apparent nest productivity and chick survival in Carbon County, Wyoming

Greater sage-grouse Centrocercus urophasianus populations across North America have been declining due to degradation and fragmentation of sagebrush habitat. As part of a study quantifying greater sage-grouse demographics prior to construction of a wind energy facility, we estimated apparent net nest productivity and survival rate of chicks associated with radio-equipped female sage-grouse in Carbon County, Wyoming, USA. We estimated apparent net nest productivity using a weighted mean of the average brood size and used a modified logistic-exposure method to estimate daily chick survival over a 70-day time period. Apparent nest productivity was 2.79 chicks per female (95% CI: 1.46–4.12) in 2011, 2.00 chicks per female (95% CI: 1.00–3.00) in 2012, and 1.54 chick per female (95% CI: 0.62–2.46) in 2013. Chick survival to 70 days post-hatch was 19.10% (95% CI: 6.22–37.42%) in 2011, 4.20% (95% CI: 0.84–12.31%) in 2012, and 16.05% (95% CI: 7.67–27.22%) in 2013. These estimates were low, yet within the range of other published survival rates. Chick survival was primarily associated with year and chick age, with minor effects of average temperature between surveys and hatch date. The variability in chick survival rates across years of our study suggests annual weather patterns may have large impacts on chick survival. Thus, management actions that increase the availability of food and cover for chicks may be necessary, especially during years with drought and above-average spring temperatures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Monitoring system for high-tech equipment

Many organizations and automated data-processing systems are involved in analysis of the logistical support (ALS) of high-tech equipment. Therefore, the initial data and the results of analysis must be presented in standardized form. That calls for an integrated informational model describing all the relevant data elements, their attributes, and their relationships. In ALS, the instruments that monitor the operation of the high-tech equipment form an important component of the information system.

42 ENGINEERING↗
DOE OSTI2018

Performance-Based Incentive Mechanisms in Life-Cycle Contracts

Within the framework of lean production, Russian industry needs to develop and introduce more effective mechanisms ensuring the fulfillment of state orders for capital- and technology-intensive systems, on the basis of performance-based logistics (PBL). In the present work, the introduction of PBL on the basis of a life-cycle contract for a specific program (product) is analyzed. One mechanism recommended in order to overcome the risks of indeterminacy is stepwise correction of incentive systems in the fulfillment of life-cycle contracts (with targeted increase in incentives for the attainment of specified results).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test

Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results: When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion: Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.

Neurosciences & Neurology↗
DOE OSTI2021

Stakeholder Support for Wildlife Conservation Funding Policies

State wildlife management agencies in the United States have depended on a “user-pay” funding model for conservation efforts that relies on revenue from hunting license sales and a federal excise tax on firearms, ammunition, and archery equipment. Declines in hunting participation, however, jeopardize sustainability of the current funding model. Ensuring support among stakeholders for wildlife management and conservation may require expanding sources of funding and incorporating the perspectives and values of a diversifying constituency into decision making processes. We used a web-based survey of wildlife-associated recreationists in Michigan, USA to evaluate support for a range of conservation funding policies. Respondents self-identified primarily as hunters (n = 2,558) or wildlife watchers (n = 942). We used binary logistic regression to evaluate support for four conservation funding policy options: state sales tax, lottery proceeds, extractive industry revenue, and a user-based tax on outdoor gear (i.e., “backpack tax”). Determinants of support varied by type of policy and stakeholder characteristics. We found no statistically significant differences between hunters and wildlife watchers in their support for conservation funding policies when accounting for other variables such as wildlife value orientations, engagement in stewardship behaviors, age, and gender. The industry-based policy achieved the greatest level of approval, while the backpack tax had the lowest. Respondents were mixed in their support of the sales tax and lottery proceeds options. Cluster analysis revealed three homogenous groups related to conservation funding policies: “strong support,” “mixed/opposed,” and “anti-backpack tax.” Clusters differed in their support for conservation funding policies and on psychological and demographic variables. The “strong support” and “anti-backpack tax” groups differed in their levels of stewardship engagement, knowledge of conservation funding mechanisms, and support for the backpack tax option. The “mixed-opposed” group tended to be older, less educated, and less likely to be a member of a conservation organization. Results suggest support for conservation funding differs by policy type and social and psychological characteristics of stakeholders. Based on differences in policy support revealed in this study, we suggest a multi-tiered approach to funding conservation and building on support among wildlife stakeholders to mitigate the looming funding crisis for state wildlife agencies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Editorial: Resolving atmospheric flow in complex environments: recent experiments in terrain and forest canopies

The characterization of atmospheric flows in complex environments, which may include steep terrain slopes and heterogeneous vegetation and/or forest cover, is a long-standing challenge in boundary-layer meteorology. Atmospheric observations are complicated by the presence of transient, terrain-induced flow features, forest-canopy-atmosphere interactions, and atmospheric stability effects, not to mention the logistical hurdles involved with instrument deployment, data analysis, and quality control. Furthermore, challenges in atmospheric modeling arise due to numerical errors associated with complex terrain flows, as well as reliance on simplified parameterizations for unresolved processes such as turbulent mixing and land-surface or forest-canopy-atmosphere interactions. These modeling challenges are exacerbated in the so-called “gray zone,” wherein features of interest have length scales that are similar to the model grid spacing, or when the principal flow layer is smaller than the grid spacing (e.g., slope flows).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Recycled Paper as a Source of Renewable Jet Fuel in the United States

Converting biomass into jet fuel involves more than the core chemical process. The overall process includes the logistics of harvesting and transporting the biomass, handling and preparing the material for processing, and processing and disposal of waste. All of these activities contribute to cost. Controlling cost involves more than developing efficient process chemistry. Choice of feedstock also has a significant impact on process economics. We consider chemical conversion of paper from municipal solid waste as a feedstock for the production of jet fuel and diesel. Paper has a significantly higher cellulose content than raw lignocellulosic biomass such as corn stover, so it requires less pretreatment to convert it into hydrocarbons than lignocellulosic biomass. Our techno-economic analysis showed that the cost of converting paper waste into jet fuel is about $1.00/gal less than jet fuel produced from corn stover. Although the cost of recycling paper into jet fuel is less than producing it from corn stover, the process is not competitive with petroleum. We estimated a minimum selling price of $3.97/gal for paper-derived jet fuel. Our sensitivity studies indicated that the biggest economic obstacle is the cost of cellulose hydrolysis. Direct hydrogenation of paper to sugar alcohols combined with increased economy of scale could make recycling paper jet fuel competitive.

09 BIOMASS FUELS↗
DOE OSTI2022

Image Analysis for Rapid Assessment and Quality-Based Sorting of Corn Stover

Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Additionally, logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.

09 BIOMASS FUELS↗
DOE OSTI2022

What Makes You Hold on to That Old Car? Joint Insights From Machine Learning and Multinomial Logit on Vehicle-Level Transaction Decisions

What makes you hold on to that old car? While the vast majority of household vehicles are still powered by conventional internal combustion engines, the progress of adopting emerging vehicle technologies will critically depend on how soon the existing vehicles are transacted out of the household fleet. Leveraging a nationally representative longitudinal data set, the Panel Study of Income Dynamics, this study examines how household decisions to dispose of or replace a given vehicle are: 1) influenced by the vehicle’s attributes, 2) mediated by households’ concurrent socio-demographic and economic attributes, and 3) triggered by key life cycle events. Coupled with a newly developed machine learning interpretation tool, TreeExplainer, we demonstrate an innovative use of machine learning models to augment traditional logit modeling to both generate behavioral insights and improve model performance. We find the two gradient-boosting-based methods, CatBoost and LightGBM, are the best performing machine learning models for this problem. The multinomial logistic model can achieve similar performance levels after its model specification is informed by TreeExplainer. Both machine learning and multinomial logit models suggest that while older vehicles are more likely to be disposed of or replaced than newer ones, such probability decreases as the vehicles serve the family longer. Pickup trucks and sport utility vehicles are less likely to be disposed of or replaced than cars, and leased vehicles are more likely to be transacted than owned vehicles. We find that married families, families with higher education levels, homeowners, and older families tend to keep their vehicles longer. Life events such as childbirth, residential relocation, and change of household composition and income are found to increase vehicle disposal and/or replacement. We provide additional insights on the timing of vehicle replacement or disposal, in particular, the presence of children and childbirth events are more strongly associated with vehicle replacement among younger parents.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Multimerization of Ebola GPΔmucin on protein nanoparticle vaccines has minimal effect on elicitation of neutralizing antibodies

Ebola virus (EBOV), a member of the Filoviridae family of viruses and a causative agent of Ebola Virus Disease (EVD), is a highly pathogenic virus that has caused over twenty outbreaks in Central and West Africa since its formal discovery in 1976. The only FDA-licensed vaccine against Ebola virus, rVSV-ZEBOV-GP (Ervebo®), is efficacious against infection following just one dose. However, since this vaccine contains a replicating virus, it requires ultra-low temperature storage which imparts considerable logistical challenges for distribution and access. Additional vaccine candidates could provide expanded protection to mitigate current and future outbreaks. Here, we designed and characterized two multimeric protein nanoparticle subunit vaccines displaying 8 or 20 copies of GPΔmucin, a truncated form of the EBOV surface protein GP. Single-dose immunization of mice with GPΔmucin nanoparticles revealed that neutralizing antibody levels were roughly equivalent to those observed in mice immunized with non-multimerized GPΔmucin trimers. These results suggest that some protein subunit antigens do not elicit enhanced antibody responses when displayed on multivalent scaffolds and can inform next-generation design of stable Ebola virus vaccine candidates.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A Double-Edged Sword: The Role of Prior Knowledge in Memory Aging

People accumulate knowledge throughout their lifespan and the accumulated knowledge influences how we encode and retrieve information in memory processing. This study aims to investigate the role of knowledge in associative memory across the adult lifespan, and specifically examines the effects of two material properties that interact with prior knowledge: congruency – whether the material is congruent with people’s prior knowledge, and ambiguity – whether the material is ambiguous to interpret based on prior knowledge. 273 participants (aged 22–70 years old) completed an incidental memory task online. Participants were shown pictures depicting an object in a scene and judged if the object was likely or unlikely to be in the particular scene. Later, in the recognition test, participants were asked to identify if the exact picture was presented earlier. The pictures were manipulated to have varying levels of congruency, meaning that some depicted likely object–scene pairs and some unlikely. We also measured how different the likely/unlikely judgment for each object–scene pair was across all participants to determine the ambiguity level of the object–scene pair: some were more likely to receive diverse responses across people, whereas others are unambiguously consistent (or inconsistent) with common knowledge shared by most people. We used mixed-effects logistic regressions to predict memory outcome for each trial as a function of age, age 2 , congruency/ambiguity, and their interactions. The object–scene pairs perceived as congruent had higher hit rates than incongruent ones, as well as higher false alarm rates, especially in middle-aged and older people. Higher ambiguity was also related to both greater true and false memory, independent of age. Finally, the effect of ambiguity only emerged when the object–scene pair was perceived incongruent. The results suggest that people rely on prior knowledge to process new information and that this reliance improves hit responses, but also induces false memories particularly for middle-aged and older people, suggesting a double-edged role of knowledge in associative memory and its disproportionate influence on memory aging. Over-reliance on knowledge in older adults, which has been suspected in other cognitive processes, may be one of the mechanisms underlying associative memory decrease in aging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine

The domestic dog, Canis familiaris, is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases. In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors. We likewise develop a tool for identifying misclassified or outlier samples in large canine oncological datasets, analogous to that which was developed for human datasets. We test a number of machine learning algorithms and found that a convolutional neural network outperformed logistic regression and random forest approaches. We use a recently developed RNA-seq-based convolutional neural network, TULIP, to test the robustness of a human-data-trained primary tumor classification tool on cross-species primary tumor prediction. Our study ultimately highlights the molecular similarities between canine and human BLCA and glioma tumors, showing that protein-coding one-to-one homologs shared between humans and canines, are sufficient to distinguish between BLCA and gliomas. The results of this study indicate that using protein-coding one-to-one homologs as the features in the input layer of TULIP performs good primary tumor prediction in both humans and canines. Furthermore, our analysis shows that our selected features also contain the majority of features with known clinical relevance in BLCA and gliomas. Our success in using a human-data-trained model for cross-species primary tumor prediction also sheds light on the conservation of oncological pathways in humans and canines, further underscoring the importance of the canine model system in the study of human disease.

60 APPLIED LIFE SCIENCES↗