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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.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Boosts in leaf-level photosynthetic capacity aid Pinus ponderosa recovery from wildfire

Forests mitigate climate change by sequestering massive amounts of carbon, but recent increases in wildfire activity are threatening carbon storage. Currently, our understanding of wildfire impacts on forest resilience and the mechanisms controlling post-fire recovery remains unresolved due to a lack of empirical data on mature trees in natural settings. Here, we quantify the physiological mechanisms controlling carbon uptake immediately following wildfire in mature individuals of ponderosa pine (Pinus ponderosa), a wide-spread and canopy-dominant tree species in fire-prone forests. While photosynthetic capacity was lower in burned than unburned trees due to an overall depletion of resources, we show that within the burned trees, photosynthetic capacity increases with the severity of damage. Our data reveal that boosts in the efficiency of carbon uptake at the leaf-level may compensate for whole-tree damage, including the loss of leaf area and roots. We further show that heightened photosynthetic capacity in remaining needles on burned trees may be linked with reduced water stress and leaf nitrogen content, providing pivotal information about post-fire physiological processes. Our results have implications for Earth system modeling efforts because measurements of species-level physiological parameters are used in models to predict ecosystem and landscape-level carbon trajectories. Finally, current land management practices do not account for physiological resilience and recovery of severely burned trees. Our results suggest premature harvest may remove individuals that may otherwise survive, irrevocably altering forest carbon balance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Ensemble Effects on Hydroxide Bond Dissociation Free Energies in Polyoxovanadate Clusters

Understanding structure-property relationships is foundational to numerous modern chemistries, such as proton-coupled electron transfer (PCET). However, an experimentally measured property is the result of the behavior from an ensemble of molecules. Neglecting ensemble effects, especially under complex chemical environments, may obfuscate these relationships and lead to discrepancies between theory and experiment. In this work, we demonstrate the impact of configurational entropy and local chemical environments on hydroxide bond dissociation free energies [BDFE- (O−H)] for a set of polyoxovanadate nanoclusters, at ambient conditions. The O−H bond strengths are investigated via density functional theory (DFT) coupled with statistical thermodynamic analysis and bilinear modeling, and compared with previous experimental results on the same systems, namely electrochemical solutions of: [V 6 O 13−x (OH) x (TRIOL R ) 2 ] −2 (x = 2, 4, 6; R = NO 2 , Me) and [V 6 O 11−x (OMe) 2 (OH) x (TRIOL NO 2 ) 2 ] −2 (x = 2, 4). Interestingly, we find that ensemble effects, even at room temperature, can account for a significant portion of the BDFE(O−H) trend with the degree of reduction via H atom binding, which cannot be fully captured by single-structure, static DFT calculations. Moreover, we find that the ensemble effects may be replicated statistically, requiring only enumeration of energetically accessible H-binding sites. With the ensemble effects resolved, we present a simple bilinear model to reconcile remaining biases between experiment and ensemble-informed theory, which corelate with clusterspecific electronic environment differences. The bilinear model achieves outstanding accuracy vs experiments with a root-mean squared error of 0.4 kcal/mol. Finally, based on the physicochemical characteristics of hydrogen interaction with polyoxometalates, we present a simple methodology that captures the BDFE(O−H) trend while dramatically reducing required DFT calculations by 98% and achieving accuracy within 1 kcal/mol. Overall, this work elucidates the roles and structural origins of configurational entropy and chemical effects on polyoxometalate hydroxide bond energies, with potential applicability to various atomically precise metal oxide systems. Importantly, it introduces models for rapid and highly accurate property calculations in connection with experiments.

Cluster chemistry↗
DOE OSTI2023

Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting

Building energy models are used to simulate heat and mass transfer and estimate end-use load in buildings. With the proliferation of solar photovoltaics on residential and commercial buildings, increasingly, buildings are expected to provide grid services, for which accurate and computationally efficient building energy simulations and end-use load prediction are imperative. Existing building energy simulation tools, however, have significant computational overhead that make them less practical in real-time deployment for optimization, design, uncertainty quantification and control in building energy management systems. Here this article presents a data-driven machine learning model based on light gradient boosting method (LightGBM) as a surrogate for a physics-based simulator for residential buildings to predict end-use load. The machine learning based surrogate model accounts for time-series related variables, seasonality and trend component of end-use load, and history of end-use load. The accuracy of the surrogate model is assessed on the prediction of the load profiles of 100 different houses in Cook County, Illinois, USA. The LightGBM surrogate model is shown to reduce the root-mean-squared error by 53% relative to a reference decision tree (DT) based model reported previously in the literature. Moreover, the model predicts the load spikes and high-ramp rate events throughout the year which are often the Achilles heel of other models in the literature. The machine learning based surrogate model is demonstrated to be computationally efficient, with a ten-fold reduction in the computational time compared to a physics-based building energy simulation, and suitable for uncertainty analysis and real-time control of building characteristics in response to uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Burning questions for a warming and changing world: 15 unknowns in plant abiotic stress

Abstract We present unresolved questions in plant abiotic stress biology as posed by 15 research groups with expertise spanning eco-physiology to cell and molecular biology. Common themes of these questions include the need to better understand how plants detect water availability, temperature, salinity, and rising carbon dioxide (CO2) levels; how environmental signals interface with endogenous signaling and development (e.g. circadian clock and flowering time); and how this integrated signaling controls downstream responses (e.g. stomatal regulation, proline metabolism, and growth versus defense balance). The plasma membrane comes up frequently as a site of key signaling and transport events (e.g. mechanosensing and lipid-derived signaling, aquaporins). Adaptation to water extremes and rising CO2 affects hydraulic architecture and transpiration, as well as root and shoot growth and morphology, in ways not fully understood. Environmental adaptation involves tradeoffs that limit ecological distribution and crop resilience in the face of changing and increasingly unpredictable environments. Exploration of plant diversity within and among species can help us know which of these tradeoffs represent fundamental limits and which ones can be circumvented by bringing new trait combinations together. Better defining what constitutes beneficial stress resistance in different contexts and making connections between genes and phenotypes, and between laboratory and field observations, are overarching challenges.

Biochemistry & Molecular Biology↗
DOE OSTI2023

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

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

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Methods for Determining the Infrared Complex Refractive Indices n (λ) and k (λ) from Organic Solid Powders: Comparison of a Sucrose Single Crystal and a Pressed Pellet

There exists a need to measure optical constants n and k for many materials, especially organic solids in the infrared spectral region, but at present, there are only two established methods for making such measurements: single-angle (SA) reflectance and infrared spectroscopic ellipsometry (IRSE). The former derives the n/k vectors via measurement of the change in the amplitude of light reflected from the sample and then uses the Kramers–Kronig transform, while the latter measures the change in polarization of the reflected light from the sample to derive n/k. Here we make a direct comparison between the n/k vectors derived from SA measurements of a sucrose single crystal and the n/k values obtained after the crystal had been crushed, ground to powder, and pressed into a pellet form. The pellet was measured not only by SA but also via the IRSE method, and we found that pressing specular-quality pellets was critical, especially for the SA data. The n/k values of this typical organic molecule all agree to within 0.04 root-mean-square error, confirming that a pressed pellet can be used instead of a crystal to derive the intrinsic n/k vectors. In conclusion, this result is important for the many organic materials that, unlike sugar, do not have centimeter-sized crystals readily available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Amino acid substrate specificities and tissue expression profiles of the nine CYP79A encoding genes in Sorghum bicolor

Cytochrome P450s of the CYP79 family catalyze two N-hydroxylation reactions, converting a selected number of amino acids into the corresponding oximes. The sorghum genome (Sorghum bicolor) harbours nine CYP79A encoding genes, and here sequence comparisons of the CYP79As along with their substrate recognition sites (SRSs) are provided. The substrate specificity of previously uncharacterized CYP79As was investigated by transient expression in Nicotiana benthamiana and subsequent transformation of the oximes formed into the corresponding stable oxime glucosides catalyzed by endogenous UDPG-glucosyltransferases (UGTs). CYP79A61 uses phenylalanine as a substrate, whereas CYP79A91, CYP79A93, and CYP79A95 use valine and isoleucine as substrates, with CYP79A93 showing the ability also to use phenylalanine. CYP79A94 uses isoleucine as a substrate. Analysis of 249 sorghum transcriptomes from two different sorghum cultivars showed the expression levels and tissue-specific expression of the CYP79As. CYP79A1 is the committed gene in dhurrin formation and was the highest expressed gene in most tissues/organs. CYP79A61 was primarily expressed in fully developed leaf blades and leaf sheaths. CYP79A91 and CYP79A92 were expressed mainly in roots >200 cm below ground, while CYP79A93 and CYP79A94 were most highly expressed in the leaf collar and leaf sheath, respectively. Here, the possible signalling effects of the oximes and their metabolites produced in different sorghum tissues are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Unraveling Bacterial Adaptation Strategies in the Microbiome Shaped by the Chemical Environment of the Plant Rhizosphere

The rhizosphere is a dynamic environment where rhizodeposits that include primary and secondary metabolites and mucilage serve as nutrient sources for soil microorganisms, attracting them toward plant roots. However, understanding how these microbes specifically respond to plant root chemical signals has been hindered by the challenges of disentangling physical and chemical interactions between the microbes and plant roots. To address this, we implemented an innovative filter-based experimental setup on plant roots that creates a physical barrier while facilitating the exchange of chemical signals. The proteomic analysis of 10 Populus root-associated bacterial strains grown in the presence or absence of a plant in either individual or mixed community conditions provided detailed insights into the functional responses of these strains to the root chemical environment. Additionally, this approach allowed us to discern the impact of root exudates on overall community dynamics. In particular, metaproteomic analyses revealed that each of these 10 microbial members responds uniquely to the presence of the plant, with Bacillus and Pantoea exhibiting the most dramatic favorable impact. Proteomic examination revealed the details of metabolism fine-tuning, including processes such as chemotaxis and ATP-binding cassette transporter proteins. This study demonstrates the application of a filter-based experimental setup to study microbial responses to plant chemicals and sheds light on adaptation strategies employed by various bacterial strains for survival in the rhizosphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The Carbon Budget of Land Conversion: Sugarcane Expansion and Implications for a Sustainable Bioenergy Landscape in Southeastern United States

The expansion of sugarcane onto land currently occupied by improved (IMP) and semi-native (SN) pastures will reshape the U.S. bioenergy landscape. We combined biometric, ground-based and eddy covariance methods to investigate the impact of sugarcane expansion across subtropical Florida on the carbon (C) budget over a 3-year rotation. With 2.3- and 5.1-fold increase in productivity over IMP and SN pastures, sugarcane displayed a C use efficiency (CUE; i.e., fraction of gross C uptake allocated to plant growth) of 0.59, well above that of pastures (0.31–0.23). Sugarcane also had greater C allocation to aboveground productivity and hence, harvestable biomass relative to IMP and SN. Cane heterotrophic respiration over the 3-year rotation (903 ± 335 gC m −2 year −1 ) was 1% and 14% higher than IMP and SN pastures, respectively. These soil C losses responded largely to disturbance over the first year after conversion (1510 ± 227 gC m −2 year −1 ) but declined in subsequent years to an average 599 ± 90 gC m −2 year −1 —well below those of IMP (933 ± 140 gC m −2 year −1 ) and SN (759 ± 114 gC m −2 year −1 ) pastures—despite a significant 40%–61% increase in soil C inputs. Soil C inputs, however, shifted from root-dominated in pastures to litter-dominated in sugarcane, with only 5% C allocation to roots. Reduced decomposition rates in sugarcane were likely driven by changes in the recalcitrance and distribution rather than the size of the newly incorporated soil C pool. As a result, we observed a rapid shift in the net ecosystem C balance (NECB) of sugarcane from a large source immediately following conversion to approaching the net C losses of IMP pastures only 2 years after conversion. The environmental cost of converting pasture to sugarcane underscores the importance of implementing management practices to harness the soil C storage potential of sugarcane in advancing a sustainable bioeconomy in Southeastern United States.

09 BIOMASS FUELS↗
DOE OSTI2023

Drought conditioning of rhizosphere microbiome influences maize water use traits

Background and Aims: Beneficial plant–microbe interactions can improve plant performance under drought; however, we know less about how drought-induced shifts in microbial communities affect plant traits. Methods: We cultivated Zea mays in fritted clay with soil microbiomes originating from contrasting environments (agriculture or forest) under two irrigation treatments (well-watered or water limited). Using this design, we investigated whether water conditioning was carried forward through the microbiome to affect a subsequent plant cohort that was subjected to either a well-watered or water limited treatment. Results: Regardless of the microbiome-origin, plants inoculated with a microbiome from a water limited legacy had traits that allowed them to avoid stress but conserve water. They produced longer roots to explore soil, generated greater soil dissolved organic carbon, potentially stimulating the microbiome, and slower soil water content loss during drought. A well-watered legacy resulted in plants that delayed permanent stomatal closure and higher photosynthetic nitrogen use efficiency. In plants with a forest-originated microbiome, a well-watered legacy and water treatment also resulted in higher rates of photosynthesis and stomatal conductance. Conclusion: These results demonstrate that soil microbiomes can be developed to influence plant drought performance, impacting crop resilience, using short-term microbial conditioning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Precise test of lepton flavour universality in W -boson decays into muons and electrons in pp collisions at $\sqrt{s}$ =13 TeV with the ATLAS detector

The ratio of branching ratios of the $W$ boson to muons and electrons, $R^{μ/e}_{W}$ = $\mathcal{B}$($W$ → $μν$)/$\mathcal{B}$($W$ → $eν$), has been measured using 140 fb -1 of pp collision data at $\sqrt{s}$ = 13 TeV collected with the ATLAS detector at the LHC, probing the universality of lepton couplings. The ratio is obtained from measurements of the $t\overline{t}$ production cross section in the $ee$, $eμ$ and $μμ$ dilepton final states. To reduce systematic uncertainties, it is normalised by the square root of the corresponding ratio $R^{μμ/ee}_{Z}$ for the Z boson measured in inclusive $Z$ → $ee$ and $Z$ → $μμ$ events. By using the precise value of $R^{μμ/ee}_{Z}$ determined from $e^+$ $e^-$ colliders, the ratio $R^{μ/e}_{W}$ is determined to be $R^{μ/e}_{W}$ = 0.9995 ± 0.0022 (stat) ± 0.0036 (syst) ± 0.0014 (ext). The three uncertainties correspond to data statistics, experimental systematics and the external measurement of $R^{μμ/ee}_{Z}$, giving a total uncertainty of 0.0045, and confirming the Standard Model assumption of lepton flavour universality in $W$ boson decays at the 0.5% level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Duke Forest FACE (FACTS-I): Plant and Soil Response Data

This dataset describing the responses of plant and soil pools and fluxes to elevated atmospheric CO2 concentration and increased nitrogen supply was collected from Duke Forest Free Air CO2 Enrichment (FACE) – Forest-Atmosphere Carbon Transfer and Storage (FACTS-I) experiment from 1996 to 2012. The dataset includes data files for allometry (diameter at breast height, tree height, and height to live crown base), leaf area index, biomass (stem, branch, foliage, and root biomass, tree density, and basal area), net primary productivity (stem, branch, foliage, reproductive, and coarse root NPP), sap flux density, soil CO2 efflux, and stem temperature. Data files were formatted as .csv (Microsoft Excel or other spreadsheet programs can be used to read the format) and file descriptions, including variable name, unit, and data range, can be found in ‘FileDescription_[data_name].txt’ files. The Duke FACE experiment was in a loblolly pine (Pinus taeda L.) plantation established in 1983. Naturally regenerated broadleaved species including sweetgum (Liquidambar styraciflua L.) and tulip poplar (Liriodendron tulipifera L.), mostly in the overstory, and winged elm (Ulmus alata Michx.) and red maple (Acer rubrum L.) were common in the understory. The FACE experiment commenced with two plots (plots 7-8) in 1994 (Oren et al. 2001), with six additional plots (plots 1-6) coming online on 27 August 1996. CO2 enrichment was terminated on 31 October 2010 and post-enrichment data collection continued through 2012. Complete fertilization was applied annually to half of plots 7-8 from 1998 to 2004. The nutrient addition experiment expanded to half of plots 1-6 with a common protocol of N-fertilization in 2005 and continued until 2012. The levels of treatment in this dataset were expressed as ambient CO2 (AMB) or elevated CO2 (ELE) for CO2 treatment and control soil (CONT) or fertilized soil (FERT) for N treatment, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Nutrient limitation shapes functional traits of mycorrhizal fungi and phosphorus-cycling bacteria across an elevation gradient

In nutrient-limited high-elevation ecosystems, plants rely on arbuscular mycorrhizal (AM) fungi to provide mineral phosphorus (P) in the form of phosphate (PO43-). AM fungi gather these nutrients from phosphorus-cycling bacteria (PCBs) that can mineralize PO43- from organic matter and solubilize mineral-bound P. How climate, soil factors, and nutrient limitation influence AM fungi and PCB assembly remains unclear. We collected soil from montane meadows across a 1,000-m elevation gradient on three replicate mountainsides and analyzed AM fungal marker genes, P-cycling genes from shotgun metagenomes, and edaphic measurements. High-elevation soils had nearly 50-fold less soil PO₄³⁻ and 60% more AM fungal hyphae than low-elevation soils. AM fungal turnover was linked to changes in pH, organic carbon, and PO₄³-. The composition of 198 P-cycling genes was influenced by the AM fungal community structure. Drivers of individual PCB functional genes, including pH and organic carbon, varied with gene phylogeny. We found a trade-off in P-cycling strategies across elevation: P-rich, low-elevation soils supported root-colonizing AM fungi and organic P-mineralizing bacteria. P-poor, high-elevation soils were dominated by stress-tolerant AM fungi and mineral P-solubilizing bacteria. Our results suggest that AM fungi and PCB community turnover across elevation are both shaped by pH, organic carbon, and P availability. With continued climate warming, the structure and function of mountaintop ecosystems might shift to resemble lower elevations, disrupting long-established and specialized microbial assemblages, with consequences for P-cycling dynamics and the total P available to plant communities.IMPORTANCEPhosphorus (P) limits plant productivity in high-elevation ecosystems, yet the microbial networks that mobilize P, including arbuscular mycorrhizal (AM) fungi and phosphorus-cycling bacteria (PCBs), remain under-characterized in these nutrient-poor soils. We show that across a 10,00-m elevation gradient, AM fungi and P-cycling gene assemblages shift predictably with pH, organic carbon, and phosphate availability. Higher elevations, with less available P, select for stress-tolerant AM fungal taxa and PCB strategies geared toward mineral solubilization, while low-elevation sites favor root colonization by AM fungi and organic P mineralization. These results suggest that nutrient limitation can constrain microbial community assembly in consistent ways across landscapes. High mountain soils are low in P and rely on a network of underground AM fungi and PCB to deliver nutrients to plants. This study shows how those underground relationships reorganize with elevation and how climate change could collapse long-standing microbial strategies by pushing high-elevation ecosystems toward lowland conditions. As soils warm and dry, the microbial scaffolding that supports alpine plant life may become increasingly unstable.

arbuscular mycorrhizal fungi↗
DOE OSTI2025

Online monitoring of lanthanide species with combined spectroscopy in flowing aqueous aerosol systems

Combined spectroscopic analysis through absorption and laser-induced breakdown spectroscopy (LIBS) was used to monitor Nd and Pr concentrations in a flowing aqueous system. A unique, online sampling approach was employed that allowed for an approximately closed analysis loop of liquids from a reservoir. Absorption spectroscopy was performed with an optical flow cell, and LIBS was performed on an aerosol stream. Multivariate calibrations based on combined absorption and LIBS signals were built for Nd and Pr and then used to monitor concentrations in mixed solutions in a series of spiking tests. In these tests, the concentrations of Nd and Pr in solution were intermittently changed while spectroscopic signals were monitored in real-time. The combined spectroscopic signals and multivariate models were successful in monitoring changing concentrations of lanthanide species with high accuracy and minimal latency. Root-mean squared error of predictions were 0.015 mol L −1 and 0.019 mol L −1 for Nd and Pr respectively, and these lanthanides were able to be monitored to an accuracy of < 0.2 wt%. This work demonstrates both the capabilities of a sampling system for online analysis of liquids and the capabilities of multimodal spectroscopic characterization for real-time, continuous tracking of species in potentially hazardous liquid systems.

absorbance spectroscopy↗
DOE OSTI2020

Neural MUSE Analysis

Researchers at Oak Ridge National Laboratory (ORNL) created data as part of the MUSE (Multi-Agency Urban Search Experiment Detector and Algorithm Test Bed) project simulating illicit nuclear materials located in various buildings along a road. In the simulation, a truck containing a radiation detector drives down the road gathering listmode data (counting the and energy of incident gamma radiation). Building materials, source shielding, driving speed, truck direction, truck location on the road, source type, and source placement are all varied between runs of the data set. This data was created using deterministic neutron transport and Monte Carlo methods through a combination of SCALE, MAVRIC, MCNP, and GADRAS. As part of a follow-on NA-22 project, two Kaggle competitions were created to determine the best algorithms for finding and identifying gamma sources in this simulated urban environment. The winning algorithm was neural network-based and had a test accuracy of 76.4% accuracy for source identification. This work seeks to build upon this work and improve the results through the application of novel machine learning techniques. As a first step, the data was classified by a simple Convolutional Neural Network (CNN) To accomplish this, the data was first preprocessed into “waterfall plots.” These plots are composed of energy vs count plots that are stacked vertically to show progression in time. The horizontal axis indicating the particle energy incorporated user defined bin spacing with options for in linear-, logarithmic-, square root-, and user-spaced bins. The z or color dimension showed the number of counts corresponding the energy-time combination. This data was then used to generate more data, by generating a local estimate of the mean of the distribution for a bin and then randomly re-sampling that bin from a Poisson distribution. Once all of this data was generated, it was fed into a well-known CNN architecture, ResNet50. The output layer of this model was removed and replaced with layers corresponding to the shape desired isotope outputs. The provided training data was used to train the classifier and the remaining testing data was used to evaluate the model. Results are soon to be forthcoming.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

A novel data gaps filling method for solar PV output forecasting

This study proposes a modified gaps filling method, expanding the column mean imputation method and evaluated using randomly generated missing values comprising 5%, 10%, 15%, and 20% of the original data on power output. The XGBoost algorithm was implemented as a forecasting model using the original and processed datasets and two sources of solar radiation data, namely, Shortwave Radiation (SWR) from Advanced Himawari Imager 8 (AHI-8) and Surface Solar Radiation Downward (SSRD) from ERA5 global reanalysis data. Further, the accuracy of the two sets of forecasted power output was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that by applying the proposed gap filling method and using SWR in forecasting solar photovoltaic (PV) output, the improvement in the RMSE and MAE values range from 12.52% to 24.30% and from 21.10% to 31.31%, respectively. Meanwhile, using SSRD, the improvement in the RMSE values range from 14.01% to 28.54% and MAE values from 22.39% to 35.53%. To further evaluate the accuracy of the proposed gap-filling method, the proposed method could be validated using different datasets and other forecasting methods. Future studies could also consider applying the said method to datasets with data gaps higher than 20%.

Energy & Fuels↗
DOE OSTI2021

First report of Seville root-knot nematode, Meloidogyne hispanica (Nematoda: Meloidogynidae) in the USA and North America

A high number of second stage juveniles of the root-knot nematode were recovered from soil samples collected from a corn field, located in Pickens County, South Carolina, USA in 2019. Extracted nematodes were examined morphologically and molecularly for species identification which indicated that the specimens of root knot juveniles were Meloidogyne hispanica. The morphological examination and morphometric details from second-stage juveniles were consistent with the original description and redescriptions of this species. The ITS rRNA, D2-D3 expansion segments of 28S rRNA, intergenic COII-16S region, nad5 and COI gene sequences were obtained from the South Carolina population of M. hispanica. Phylogenetic analysis of the intergenic COII-16S region of mtDNA gene sequence alignment using statistical parsimony showed that the South Carolina population clustered with Meloidogyne hispanica from Portugal and Australia. To our best knowledge, this finding represents the first report of Meloidogyne hispanica in the USA and North America.

59 BASIC BIOLOGICAL SCIENCES↗