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At least 1,261 records · Page 70

Geographically Dependent Sustainability Indicators for Comparison of Conventional Vegetable Production to Controlled-Environment Agriculture

Many food system lifecycle analyses distill agricultural production and supply chain impacts into single-value sustainability metrics for various food categories. These studies provide an overview of the food supply system that highlight striking results such as the GHG impacts of meat production. The supply chain impacts of vegetable production occupy the middle ground; higher than average food loss and waste, lower than average overall energy use, etc. However, aggregated results obscure the sustainability implications of the location of food production, especially for vegetables. Moreover, the increasing instability of food production systems are not captured, as illustrated by a recent Washington Post article. According to a UC Merced study conducted for the state, California farmers left nearly 400,000 acres of agricultural land unplanted last year because of a lack of water. The result, the study found, was a direct economic cost to farmers of $1.1 billion and the loss of nearly 9,000 agricultural jobs. (The Washington Post, March 21, 2022.) Previous work quantified the food-energy-water nexus implications of transitioning vegetable production from large, centralized agricultural operations to smaller distributed production in controlled-environment farms (CEA). The importance of location-specific data is especially evident for water use. Water impacts of the food system are primarily local to the region where food is produced, water impacts vary significantly between locations, and water stress is a major concern in locations that currently host large agricultural operations. Location-specific data is needed to accurately assess the water impacts of transitioning to CEA. The location of farms in relation to consumers impacts transportation energy use, food loss and waste, and requirements for food processing (e.g., to reduce weight, preserve foods for long storage, and package foods to reduce damage and loss) Reducing transport is particularly relevant for agricultural products that could be grown in CEAs (fruits, vegetables, protein). Access to nutritious food is not evenly distributed in the population. Remote communities, communities in harsh environments, and economically-disadvantaged communities often have poor access to healthy foods. CEA is ideally suited to these environments. However, the energy and water use of CEA, while in many respects lower overall than conventional supply chains, are concentrated within communities and could have significant local impacts. This paper reports on development of sustainability metrics that seek to capture the tradeoffs between the current supply chain and a CEA supply chain for vegetables; focusing on the location-dependent implications of water use, the transition from largely fossil fuel based energy use to electricity, and the food access, resilience and wellbeing implications of urban versus rural food production.

controlled-environment agriculture↗

Building Alternative Indices of Socioeconomic Status for Population Modeling in Data-Sparse Contexts (Short Paper)

Population modeling requires clear definitions of socioeconomic status (SES) to ensure overall estimate accuracy and locate potentially underserved subpopulations. This presents a challenge as SES can be measured in myriad ways and for divergent purposes, and the data required to calculate these metrics may be lacking, particularly in low and middle income countries (LMICs). To support more refined SES measurement, we explore improvements upon the Demographic and Health Survey’s (DHS) Wealth Index (DHS-WI) using alternative characterizations of SES based on multiple correspondence analysis (MCA) and hierarchical clustering. We produce the MCA-derived metrics first on a full suite of household economic, demographic, and dwelling variables, then on a reduced set of occupant-only SES characteristics. We explore the utility of these metrics relative to DHS-WI based on their ability to 1) differentiate DHS household types and 2) identify mixtures of SES levels within DHS samples and mapped at high spatial resolution. We find that our full suite MCA yields more clearly defined SES segments and that our reduced MCA delineates occupant SES most clearly, suggesting potential pathways to improve upon the DHS-WI in future population modeling efforts for LMICs.

Cunningham, Angela↗

Minimal contact slide for touch indication

A nozzle holder assembly for a three-dimensional printer comprises a mount, a printer nozzle, and a locking mechanism. The mount is operable to be secured to the three-dimensional printer. The printer nozzle shaft is movably coupled to the mount along a predetermined length. The locking mechanism is configured to fix the printer nozzle shaft relative to the mount at any position along the predetermined length.

Pino, Ruben A.↗

Control System Upgrade for Battery State-of-Charge Indications

The Advanced Test Reactor (ATR) Complex at Idaho National Laboratory (INL) relies on Battery Backed Power (BBP) systems and Uninterruptible Power Supplies (UPS) to ensure continuous power supply to critical components. This project aims to enhance the reliability and functionality of the battery monitoring and control systems by updating the State-of-Charge (SOC) system, Programmable Logic Controller (PLC), and Human-Machine Interface (HMI) for the nuclear safety-related battery banks. The current system, while functional, has areas for improvement, particularly in recharging calculations and alarm functions. The project objectives include developing flow charts, programming the new PLC and HMI, conducting bench tests, and updating design documentation. Additionally, the project ensures compliance with safety standards, develops training materials, creates comprehensive documentation, and integrates seamlessly with existing ATR infrastructure. The new SOC system is designed to be scalable for future upgrades, improve efficiency, enhance data accuracy, implement redundancy features, and achieve project goals within budget constraints while considering environmental impact. The methodology involved familiarizing with BBP and UPS systems, collecting current readings, rescaling signals, learning ladder logic, and updating the HMI. The transition from SLC 5/03 PLC using RS Logix 500 to CompactLogix 5380 using Studio 5000 was a key step. Despite challenges in transferring outdated PLC ladder logic and HMI code, starting from scratch led to a more accurate and efficient monitoring system, contributing to improved safety and operational efficiency. The project is currently awaiting approval of the Engineering Calculation and Analysis Report (ECAR) before implementation.

42 - ENGINEERING↗

Assessing the Transferability of Statistical Predictive Models for Leaf Area Index Between Two Airborne Discrete Return LiDAR Sensor Designs Within Multiple Intensely Managed Loblolly Pine Forest Locations in the South-Eastern USA

Leaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices was assessed for correlation with each other, and was used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all returns produced the strongest correlations, such as the Above and Below Ratio Index (ABRI) and Laser Penetration Index 1 (LPI1). The regression model produced from a combination of three metrics did not improve correlations greatly (R2 0.90; S.E. 0.35). The results indicate that LAI can be predicted over a range of intensively managed pine plantation forest environments accurately when using different LiDAR sensor designs. Those indices which incorporated counts of specific return numbers (e.g. first returns) or return intensity correlated poorly with field measurements. There were disparities between the number of different types of returns and intensity values when comparing the results from two LiDAR sensors, indicating that predictive models developed using such metrics are not transferable between datasets with different acquisition parameters. Each of the indices were significantly correlated with one another, with one exception (LAI proxy), in particular those indices calculated from all returns, which indicates similarities in information content for those indices. It can then be argued that LiDAR indices have reached a similar stage in development to those calculated from optical-spectral sensors, but which offer a number of advantages, such as the reduction or removal of saturation issues in areas of high biomass.

Sumnall, Matthew↗

A dataset of eco-evidence tools to inform early-stage environmental impact assessments of hydropower development

The datasets described herein provide the foundation for a decision support prototype (DSP) toolkit aimed at assisting stakeholders in determining evidence of which aspects of river ecosystems have been impacted by hydropower. The DSP toolkit and its application are presented and described in the article “Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development” [1]. Development of the DSP and the output for decision support centralize around 42 river function indicators describing the dimensionality of river ecosystems through six main categories: biota and biodiversity, water quality, hydrology, geomorphology, land cover, and river connectivity. Three main tools are represented in the DSP: A science-based questionnaire (SBQ), an environmental envelope model (EEM), and a river function linkage assessment tool (RFLAT). The SBQ is a structured survey-style questionnaire whose objective is to provide evidence of which indicators have been impacted by hydropower. Based on a global literature review, 140 questions were developed from general hypotheses regarding the impacts of dams on rivers. The EEM is a model to predict the likelihood of hydropower impacting indicators based on a several variables. The intended use of the EEM is for situations of new hydropower development where results of the SBQ are incomplete or highly uncertain. The EEM was developed through the compilation of a dataset containing attributes of dams, reservoirs, and geospatial information on environmental concerns, which was combined with data on ecological indicators documented at those sites through literature review. The model operates through 247 “envelopes” and weighting factors, representing the individual effect of each variable on each indicator, all available through spreadsheets. Finally, the RFLAT is a tool to examine causal relationships amongst indicators. Inter-indicator relationships were hypothesized based on literature review and summarized into node and edge datasets to represent the structure of a graphical network. Bayes theorem was used estimate conditional probabilities of inter-indicator relationships based on the output of the SBQ. Nodes and edges were imported into R programming environment to visualize ecological indicator networks. The datasets can be expanded upon and enriched with more detailed questions for the SBQ, building upon the EEM with to develop more sophisticated models, and identifying new relationships for the RFALT. Additionally, once the tools are applied to numerous hydropower developments, the output of the tools (e.g. evidence of impacted indicators) becomes a very useful dataset for meta-analyses of hydropower impacts.

13 HYDRO ENERGY↗

Data for Immediate Impacts of Soybean Cover Crop on Bacterial Community Composition and Diversity in Soil Under Long-Term Saccharum Monoculture

Saccharum yield decline results from long-term monoculture practices. Changes in cropping management can improve soil health and productivity. Below-ground bacterial community diversity and composition across soybean (Glycine max (L.) Merr) cover crop, Saccharum monoculture (30+ year) and fallowed soil were determined. Near full length (~1,400 base pairs) of 16S rRNA gene sequences were extracted from the rhizospheres of sugarcane and soybean and fallowed soil were compared. Higher soil bacterial diversity was observed in the soybean cover crop than sugarcane monoculture across all measured indices (observed operationational taxonomic units, Chao1, Shannon, reciprocal Simpson and Jackknife). Acidocateria, Proteobacteria, Bacteroidetes and Planctomycetes were the most abundant bacterial phyla across the treatments. Indicator species analysis identified nine indicator phyla. Planctomycetes, Armatimonadetes and candidate phylum FBP were associated with soybean; Proteobacteria and Firmicutes were linked with sugarcane and Gemmatimonadetes, Nitrospirae, Rokubacteria and unclassified bacteria were associated with fallowed soil. Non-metric multidimensional scaling analysis showed distinct groupings of bacterial operational taxonomic units (97% identity) according to management system (soybean, sugarcane or fallow) indicating compositional differences among treatments. This is confirmed by the results of the multi-response permutation procedures (A = 0.541, p = 0.00045716). No correlation between soil parameters and bacterial community structure was observed according to Mantel test (r = 211865, p = 0.14). Use of soybean cover-crop fostered bacterial diversity and altered community structure. This indicates cover crops could have a restorative effect and potentially promote sustainability in long-term Saccharum production systems.

Field Data↗

Wipes, Coatings, and Patches for Detecting Hydrazines

Three color-indicating devices have been conceived as simple, rapid, inexpensive means of detecting hazardous liquid and gaseous substances in settings in which safety is of paramount concern and it would be too time-consuming or otherwise impractical to perform detection by use of such instruments as mass spectrometers. More specifically, these devices are designed for detecting hypergolic fuels (in particular, hydrazines) and hypergolic oxidizers in spacecraft settings, where occasional leakage of these substances in liquid or vapor form occurs and it is imperative to take early corrective action to minimize adverse health effects. With suitable redesign, including reformulation of their color indicator chemicals, these devices could be adapted to detection of other hazardous substances in terrestrial settings (e.g., industrial and military ones). One of the devices is a pad of a commercially available absorbent material doped with a color indicator. The absorbent material is made from 70 percent polyester and 30 percent nylon and can absorb about eight times its own weight of liquid. The color indicator is a mixture of conventional pH color indicator chemicals. Hydrazine and monomethyl hydrazine, which are basic, cause the color indicator to turn green. In the original intended application, the pad is wiped on a space suit that is suspected of having been exposed to leaking monomethyl hydrazine during a space walk, before the wearer returns to the interior of the spacecraft. If the wiped surface is contaminated with hydrazine, the pad turns green. In addition, the pad absorbs hydrazine from the wiped surface, thereby reducing or eliminating the hazard. Used pads, including ones that show contamination by hydrazine, can be stored in a sealed plastic bag for subsequent disposal. The second device, which has been proposed but not yet developed, would comprise a color indicator material in the form of either a coating on a space suit (or other protective garment) or a coating on a sheet that could be easily attached to and detached from the protective garment. The coating material would be a hydrogel doped with a suitable pH indicator. The hydrogel would also serve to maintain a level of moisture needed to support the chemical reaction mentioned in the next sentence. In addition to changing color to indicate the presence of any hypergolic fuel (which is basic) or hypergolic oxidizer (which is acidic) that might splash on the space suit, the pH indicator would also react with the hypergolic fuel or oxidizer and thereby bind it. The third device is a color dosimeter for detecting hydrazine liquid or vapor coming from microscopic leaks. This device is designed to satisfy several requirements specific to its original intended use in the auxiliary power unit of the space shuttle. These requirements include stability under vacuum, stability at moderate temperature, fast and irreversible change in color upon exposure to hydrazine, and visibility of the color change through polyimide tape.

Young, Rebecca↗

Comparative Study of foF2 Measurements with IRI-2007 Model Predictions During Extended Solar Minimum

The unusually deep and extended solar minimum of cycle 2324 made it very difficult to predict the solar indices 1 or 2 years into the future. Most of the predictions were proven wrong by the actual observed indices. IRI gets its solar, magnetic, and ionospheric indices from an indices file that is updated twice a year. In recent years, due to the unusual solar minimum, predictions had to be corrected downward with every new indices update. In this paper we analyse how much the uncertainties in the predictability of solar activity indices affect the IRI outcome and how the IRI values calculated with predicted and observed indices compared to the actual measurements.Monthly median values of F2 layer critical frequency (foF2) derived from the ionosonde measurements at the mid-latitude ionospheric station Juliusruh were compared with the International Reference Ionosphere (IRI-2007) model predictions. The analysis found that IRIprovides reliable results that compare well with actual measurements, when the definite (observed and adjusted) indices of solar activityare used, while IRI values based on earlier predictions of these indices noticeably overestimated the measurements during the solar minimum.One of the principal objectives of this paper is to direct attention of IRI users to update their solar activity indices files regularly.Use of an older index file can lead to serious IRI overestimations of F-region electron density during the recent extended solar minimum.

Ionosphere↗

Final Report on the Detection of Green Monopropellants

In 2014, National Aeronautics and Space Administration (NASA) Kennedy Space Center (KSC) funded a project titled "Familiarization and Detection of Green Monopropellants" utilizing Independent Research and Technology Development (IR&TD) and Center Innovation Fund (CIF) funding. The purpose of the project was to evaluate methods of detection for ammonium dinitramide (ADN) and hydroxylammonium nitrate (HAN). An Engineering Services Contract (ESC) task order was created with the scope of evaluation of several methods of detecting ADN- and HAN-based propellants, as well as development of methods for detection. Detection methods include developed methods such as colorimetric indicating absorbent socks, and commercial-off-the- shelf (COTS) units for ammonia detection. An additional goal of the task order was for ESC to become familiar with ADN's and HAN's material properties and material compatibility. Two approaches were initially investigated as possible methods for the detection of HAN (or AFM315E) and ADN (or LMP-103S). These approaches were colorimetric analysis and instrumentation-based COTS vapor sensors utilization. Initial testing showed that the relatively non-existent vapor pressure of the AF-M315E (of which HAN is a major component) propellant would make the use of COTS sensors difficult for real-time area monitoring of HAN; a small response was detected through the use of active COTS sensors, including the RAE Systems MultiRAE Lite and Drager X-act (registered) 5000, but the levels detected were below the threshold limit value for the toxic gas ammonia. Therefore, a detection system ased upon a colorimetric indicator impregnated into an absorbent material was developed. Preliminary analysis (ESC-245-FDG-001) identified a particularly outstanding candidate as a colorimetric indicator for the detection of the presence of AF-M315E in the form of a Methyl Red (Basic) indicator. Materials impregnated with this indicator exhibit significant color change and the materials are not susceptible to interference from exposure to water or carbon dioxide. The completed detection system for HAN/AF-M315E consists of absorbent socks packed with Fisher Universal Spill Absorbent capable of absorbing and containing any propellant spills that they come into contact with along with indicating wipes. The absorbent socks are also chemically treated with a Methyl Red (Basic) indicator solution to provide the end user with a visual indication that a leak has occurred and proper protective precautions must be undertaken. An added benefit of this detection system is that the absorbent socks should neutralize/absorb any commodity that it comes into contact with (until saturation is reached). Additional adsorbent socks can be deployed until a color change is not seen, indicating that the HAN/AF-M315E contamination has been contained. The indicating wipes provide the user the opportunity to wipe surfaces to determine if there is any HAN/AF-M315E or HAN/AFM315E residue present. The wipes should allow the detection of fuel levels that may be too small to detect with the absorbent socks. The development of a detection system for the ADN/LMP-103S focused on the use of various COTS sensors used as real-time area monitoring devices and personal dosimeters. These COTS based sensor systems were of several different types, including both actively pumped and diffusion-based passive systems, as well as a "rope"-type chemical sensing cable. The results highlighted some of the major differences between the two monopropellants undergoing evaluation. Unlike HAN, ADN (which is the major constituent of LMP-103S) exhibits a much more volatile nature in comparison to AF-M315E. In fact, testing showed that a large percentage of the fuel was lost during the sampling measurement (greater than 10 percent by mass); although this testing cannot tell if the volatile component is the ADN itself or another component of the monopropellant solution. Not surprisingly, all four of the procured vapor-based COTS sensors showed positive results when exposed to solutions of the LMP-103S (ESC-245-FDG-002). The completed detection system for ADN/LMP-103S consists of a combination of two of the tested COTS sensor systems, the RAE Systems MultiRAE Lite and the BW Technologies GasAlert Extreme. These systems are meant to be used in conjunction with one another, which allows for the end-user to have both real-time area monitoring (MultiRAE Lite) as well as a personal dosimeter device (GasAlert Extreme) which can be worn as additional personal protective equipment. An stainless steel extension wand was fabricated and included in the detection system for the MultiRAE Lite to allow for more remote sensing, and connects via the active pumping inlet of the sensor. As stated, the final results of this testing resulted in the production of two "kits" which can be used for the detection of HAN/AF-M315E and ADN/LMP-103s (ESC-245-FDG-003).

Detection↗

Algorithmic Detection of Elemental Biosignatures

Machine learning models that classify a sample as indicative or non-indicative of life could play an important role in life-detection missions. Their predictions result from agnostic algorithms and thereby add redundancy to judgements resulting from human expertise. Additionally, their important features can reveal the most informative measurements within the operational constraints of a life-detection mission. The Ladder of Life Detection (Neveu 2018) identifies the need for an understanding of how combinations of multiple biosignatures affect overall confidence. The present work provides a starting point to answer this need, and future work will expand the data types to obtain even more predictive combinations of features. Elemental abundance was chosen as a starting set of features due to its availability in diverse sample types, which are needed to train a generalizable model. A standardized dataset was collected, including 35 non-indicative, e.g., lunar rock, basalt; 19 indicative mixed, e.g., seawater, agricultural soil; 46 indicative non-alive, e.g., coal, chalk; and 10 indicative alive, e.g., biofilm, bacteria. This dataset could be valuable for complementary biosignature research. The samples were standardized to the same limit of detection of a simulated mission scenario. Four classification models were used: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), and Gaussian naïve Bayes (GNB). To obtain feature importances, KNN was run on three principal components of the training data and LR and SVM were run with L1 and L2 regularization. The performances and feature importances of the six model variants on 40:60 train to validation ratios were assessed with Monte Carlo simulations. ROC AUC and mean accuracy scores ranged between 82% - 94%, with sensitivity greater than specificity. For indicative of life predictors, all models had C and Ca as strong and Cl as medium; a majority of models had N, K, and P as medium. For non-indicative of life predictors, all models had Si as strong, and a majority of models had Mg, Al, and Ti as medium. Varied elements were Fe (slightly non-indicative), H (slightly indicative), O (widely varied), Na, Mn, and S. These results serve as a proof of concept and suggest important elemental signals beyond merely the CHNOPS of Earth-based life.

Algorithmic↗