Predict ephemeral and perennial range quantity and quality during normal grazing season
There are no author-identified significant results in this report.
Bentley, G.↗
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Predict ephemeral and perennial range quantity and quality during normal grazing season
There are no author-identified significant results in this report.
Bentley, G.↗
Predict ephemeral and perennial range quantity and quality during normal grazing season
There are no author-identified significant results in this report. Analysis of imagery made during the second and third 2-month periods has yielded little usable information for the following reasons. Imagery taken of the deserts in Arizona and California and semi-arid southeastern Oregon has been of poor quality. Because of high reflectance of light from barren desert soils, imagery is over-exposed and positive transparencies are washed out. Imagery was also taken when plants were dormant so that differences in vegetation growth and development as exhibited by changes in tone of color infrared are nonexistent.
Bentley, G.↗
Predict ephemeral and perenniel range quantity and quality during normal grazing season
There are no author-identified significant results in this report.
Bentley, G.↗
Predict ephemeral and perennial range quantity and quality during normal grazing season
There are no author-identified significant results in this report.
Bentley, G.↗
Predict ephemeral and perennial range quantity and quality during normal grazing season
There are no author-identified significant results in this report.
Bentley, G.↗
Predict ephemeral and perennial range quantity and quality during normal grazing season
The author has identified the following significant results. Collection and update of resource inventory data has historically been a difficult, time consuming task. Accurate resource data is necessary as a basis for wise management decisions made by a resource management agency such as the Bureau of Land Management. Black and white and color infrared composites of ERTS satellite imagery at 1:1,000,000 and enlarged scales can be used as data gathering tools. No investment in expensive sophisticated equipment is necessary. A photointerpreter can map boundaries of soils, plant communities, levels of forage production, areas revegetated by man, and areas burned by wildlife directly from satellite imagery. The ERTS system of producing and distributing imagery must be improved greatly before satellite imagery can be useful to the resource manager.
Bentley, R. G., Jr.↗
Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics
Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.
25 ENERGY STORAGE↗
Predicted Turbine Heat Transfer for a Range of Test Conditions
Comparisons are shown between predictions and experimental data for blade and endwall heat transfer. The comparisons of computational domain parisons are given for both vane and rotor geometries over an extensive range of Reynolds and Mach numbers. Comparisons are made with experimental data from a variety of sources. A number of turbulence models are available for predicting blade surface heat transfer, as well as aerodynamic performance. The results of an investigation to determine the turbulence model which gives the best agreement with experimental data over a wide range of test conditions are presented.
Boyle, R. J.↗
Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions
Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.
crop-model ensembles↗
Minnesota peat viromes reveal terrestrial and aquatic niche partitioning for local and global viral populations
Abstract Background Peatlands are expected to experience sustained yet fluctuating higher temperatures due to climate change, leading to increased microbial activity and greenhouse gas emissions. Despite mounting evidence for viral contributions to these processes in peatlands underlain with permafrost, little is known about viruses in other peatlands. More generally, soil viral biogeography and its potential drivers are poorly understood at both local and global scales. Here, 87 metagenomes and five viral size-fraction metagenomes (viromes) from a boreal peatland in northern Minnesota (the SPRUCE whole-ecosystem warming experiment and surrounding bog) were analyzed for dsDNA viral community ecological patterns, and the recovered viral populations (vOTUs) were compared with our curated PIGEON database of 266,125 vOTUs from diverse ecosystems. Results Within the SPRUCE experiment, viral community composition was significantly correlated with peat depth, water content, and carbon chemistry, including CH 4 and CO 2 concentrations, but not with temperature during the first 2 years of warming treatments. Peat vOTUs with aquatic-like signatures (shared predicted protein content with marine and/or freshwater vOTUs) were significantly enriched in more waterlogged surface peat depths. Predicted host ranges for SPRUCE vOTUs were relatively narrow, generally within a single bacterial genus. Of the 4326 SPRUCE vOTUs, 164 were previously detected in other soils, mostly peatlands. None of the previously identified 202,371 marine and freshwater vOTUs in our PIGEON database were detected in SPRUCE peat, but 0.4% of 80,714 viral clusters (VCs, grouped by predicted protein content) were shared between soil and aquatic environments. On a per-sample basis, vOTU recovery was 32 times higher from viromes compared with total metagenomes. Conclusions Results suggest strong viral “species” boundaries between terrestrial and aquatic ecosystems and to some extent between peat and other soils, with differences less pronounced at higher taxonomic levels. The significant enrichment of aquatic-like vOTUs in more waterlogged peat suggests that viruses may also exhibit niche partitioning on more local scales. These patterns are presumably driven in part by host ecology, consistent with the predicted narrow host ranges. Although more samples and increased sequencing depth improved vOTU recovery from total metagenomes, the substantially higher per-sample vOTU recovery after viral particle enrichment highlights the utility of soil viromics.
59 BASIC BIOLOGICAL SCIENCES↗
Mobile range control center/range safety command destruct and real-time instantaneous impact prediction system
The mobile range control center/range safety command destruct and real time instantaneous impact prediction system (MRCCS), designed for launching sounding rockets and expendable launch vehicles, is described. The MRCCS provides mission control and range safety support for remote launch sites and is equipped with telemetry and radar data acquisition systems, real time data computation systems, range safety display systems, and command destruct transmitters.
Hudson, Sandra Merritt↗
Rocket Launch-Induced Vibration and Ignition Overpressure Response
Rocket-induced vibration and ignition overpressure response environments are predicted in the low-frequency (5 to 200 hertz) range. The predictions are necessary to evaluate their impact on critical components, structures, and facilities in the immediate vicinity of the rocket launch pad.
Caimi, Raoul E.↗
Rocket Launch-Induced Vibration and Ignition Overpressure Response
Rocket-induced vibration and ignition overpressure response environments are predicted in the low-frequency (5 to 200 hertz) range. The predictions are necessary to evaluate their impact on critical components, structures, and facilities in the immediate vicinity of the rocket launch pad.
Caimi, Raoul↗
Unstructured Grid Viscous Flow Simulation Over High-Speed Research Technology Concept Airplane at High-Lift Conditions
Numerical viscous solutions based on an unstructured grid methodology are presented for a candidate high-speed civil transport configuration, designated as the Technology Concept Airplane (TCA), within the High-Speed Research (HSR) program. The numerical results are obtained on a representative TCA high-lift configuration that consisted of the fuselage and the wing, with deflected full-span leading-edge and trailing-edge flaps. Typical on-and off-surface flow structures, computed at high-lift conditions appropriate for the takeoff and landing, indicated features that are generally plausible. Reasonable surface pressure correlations between the numerical results and the experimental data are obtained at free-stream Mach number M(sub infinity) = 0.25 and Reynolds number based on bar-c R(sub c) = 8 x 10(exp 6) for moderate angles of attack of 9.7 deg. and 13.5 deg. However, above and below this angle-of-attack range, the correlation between computed and measured pressure distributions starts to deteriorate over the examined angle-of-attack range. The predicted longitudinal aerodynamic characteristics are shown to correlate very well with existing experimental data across the examined angle-of-attack range. An excellent agreement is also obtained between the predicted lift-to-drag ratio and the experimental data over the examined range of flow conditions.
Ghaffari, Farhad↗
Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors
Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material’s target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal–organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions.
36 MATERIALS SCIENCE↗
Correlated Anion Disorder in Heteroanionic Cubic TiOF 2
Resolving anion configurations in heteroanionic materials is crucial for understanding and controlling their properties. For anion-disordered oxyfluorides, conventional Bragg diffraction cannot fully resolve the anionic structure, necessitating alternative structure determination methods. We have investigated the anionic structure of anion-disordered cubic (ReO 3 -type) TiOF 2 using X-ray pair distribution function (PDF), 19 F MAS NMR analysis, density functional theory (DFT), cluster expansion modeling, and genetic-algorithm structure prediction. Our computational data predict short-range anion ordering in TiOF 2 , characterized by predominant cis-[O 2 F 4 ] titanium coordination, resulting in correlated anion disorder at longer ranges. To validate our predictions, we generated partially disordered supercells using genetic-algorithm structure prediction and computed simulated X-ray PDF data and 19 F MAS NMR spectra, which we compared directly to experimental data. To construct our simulated 19 F NMR spectra, we derived new transformation functions for mapping calculated magnetic shieldings to predicted magnetic chemical shifts in titanium (oxy)fluorides, obtained by fitting DFT-calculated magnetic shieldings to previously published experimental chemical shift data for TiF 4 . We find good agreement between our simulated and experimental data, which supports our computationally predicted structural model and demonstrates the effectiveness of complementary experimental and computational techniques in resolving anionic structure in anion-disordered oxyfluorides. From additional DFT calculations, we predict that increasing anion disorder makes lithium intercalation more favorable by, on average, up to 2 eV, highlighting the significant effect of variations in short-range order on the intercalation properties of anion-disordered materials.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Improving deep learning-based protein distance prediction in CASP14
Abstract Motivation Accurate prediction of residue–residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Results Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions [i.e. classifying distances between two residues into two categories: in contact (<8 Angstrom) and not in contact otherwise] and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps. Availability and implementation The software package, source code and data of DeepDist2 are freely available at https://github.com/multicom-toolbox/deepdist and https://zenodo.org/record/4712084#.YIIM13VKhQM. Supplementary information Supplementary data are available at Bioinformatics online.
59 BASIC BIOLOGICAL SCIENCES↗