Dynamic wastewater-induced research based on input-output analysis for Guangdong Province, China
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A readily parallelized Maximum Likelihood Estimation (MLE) algorithm with linear computational complexity is demonstrated in real time using only measurements from an extreme ultraviolet (EUV) diagnostic to control the horizontal position of a tokamak plasma. A set of trial emissivity profiles are parameterized by the control quantity of interest (R m ), and the MLE is identified from the profile which minimizes the signal reconstruction residual. The algorithm depends on an empirically determined likelihood function with exponential form. EUV emission (λ ≈ 15eV-1keV) is captured in a poloidal plane by four 16-channel AXUV diodes mounted at different poloidal angles with radial and angular resolution sufficient to discern plasma equilibrium evolution in HBT-EP. Calculations of the plasma major radius by the system are consistent within diagnostic uncertainty for the majority of the discharge with those of: a weighted average of vertical soft X-ray or EUV chords, magnetic sensors, and an equilibrium reconstruction. The feedback system corrects for a horizontal displacement of the major radius equal to 20% of the plasma minor radius by adjusting the vertical field produced from 40 in-vessel control coils in real time. The MLE calculation is performed on a GPU in a 15 μs cycle, with similar performance in this application to a simple weighted average of vertical chords. Finally, results demonstrate horizontal position control using magnetic actuators and an optical observer.
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Increased atmospheric nitrogen (N) deposition can have wide-ranging effects on plant community structure and ecosystem function, some of which may be indirectly mediated by soil microbial responses to an altered biogeochemical environment. In this study, soils from a field N fertilization experiment that spanned a soil texture gradient were used as inocula in the greenhouse to assess the indirect effects of soil microbial communities on growth of a desert grass. Plant performance and interaction with soil microbiota were evaluated via plant above- and belowground biomass, leaf N concentration, and root fungal colonization. Nitrogen fertilization in the field increased the benefits of soil microbial inoculation to plant leaf N concentration, but did not alter the effect of soil microbes on plant growth. Plant-microbe interaction outcomes differed most strongly among sites with different soil textures, where the soil microbial community from the sandiest site was most beneficial to host plant growth. In conclusion, the findings of this study suggest that in a desert grassland, increases in atmospheric N deposition may exert a more subtle influence on plant-microbe interactions by altering plant nutrient status, whereas edaphic factors can alter the whole-plant growth response to soil microbial associates.
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The SNS target module is a stainless-steel structure that contains and directs mercury. The mercury is struck with short, intense proton pulses to create neutrons. The pulses deposit energy and cause loads on the target structure and mercury cavitation. Helium bubbles are introduced into the mercury to reduce loads and cavitation. This gas complicates the prediction of the target module’s physical response. The current method used to simulate the structural response to the proton pulse incorporates a simple but effective approach to approximate mercury cavitation effects. This method cannot account for the changes from injected non-condensable gas. The ability to model the target vessel response is a prerequisite for estimating its life. Known approaches for simulating a bubbly fluid mixture’s response are computationally expensive and impractical for detailed engineering models. A constitutive model is proposed for target mercury with injected gas bubbles, with the intent of providing more accurate mercury–vessel response behavior without the computing costs required to track individual bubbles. The model’s assumptions and a computational model embodiment are described. The bubbly mercury computational model is subjected to test problems to assess its potential utility. Here, the model was able to better predict the effect of gas on wave propagation.
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Peatlands are climate critical carbon (C) reservoirs that could become a C source under continued warming. A strong relationship between plant tissue chemistry and the soil organic matter (SOM) that fuels C gas emissions is inferred, but rarely examined at the molecular level. Here we compared Fourier transform infrared (FT-IR) spectroscopy measurements of solid phase functionalities in plants and SOM to ultra-high-resolution mass spectrometric analyses of plant and SOM water extracts across a palsa-bog-fen thaw and moisture gradient in an Arctic peatland. From these analyses we calculated the C oxidation state (NOSC), a measure which can be used to assess organic matter quality. Palsa plant extracts had the highest NOSC, indicating high quality, whereas extracts of Sphagnum, which dominated the bog, had the lowest NOSC. The percentage of plant compounds that are less bioavailable and accumulate in the peat, increases from palsa (25%) to fen (41%) to bog (47%), reflecting the pattern of percent Sphagnum cover. The pattern of NOSC in the plant extracts was consistent with the high number of consumed compounds in the palsa and low number of consumed compounds in the bog. However, in the FT-IR analysis of the solid phase bog peat, carbohydrate content was high implying high quality SOM. Here, we explain this discrepancy as the result of low solubilization of bog SOM facilitated by the low pH in the bog which makes the solid phase carbohydrates less available to microbial decomposition. Plant-associated condensed aromatics, tannins, and lignin-like compounds declined in the unsaturated palsa peat indicating decomposition, but lignin-like compounds accumulated in the bog and fen peat where decomposition was presumably inhibited by the anaerobic conditions. A molecular-level comparison of the aboveground C sources and peat SOM demonstrates that climate-associated vegetation shifts in peatlands are important controls on the mechanisms underlying changing C gas emissions.