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The Abrasion of Photovoltaic Glass: A Comparison of the Effects of Natural and Artificial Aging
Natural soiling and the subsequent requisite cleaning of photovoltaic (PV) modules result in abrasion damage to the cover glass. The durability of the front glass has important economic consequences, including determining the use of antireflective and/or antisoiling coatings as well as the method and frequency of operating maintenance (cleaning). The abrasion of coatings and glass has been explored in a field study, including the soiling-prone locations of Dubai (United Arab Emirates), Kuwait City (Kuwait), Mesa (Arizona), Mumbai (India), and Sacramento (California). Dry-brush-cleaned specimens will be compared to those subjected to artificial-brush testing. The characteristics of material integrity, surface energy, optical transmittance, surface roughness, and scratch size were examined using an optical microscope, contact goniometer (for water), spectrophotometer, interferometer, and atomic force microscope (AFM), respectively. The findings of this study will be used to provide feedback regarding the cleaning equipment, cleaning methods, and coatings used in the PV industry. The study here will also be used to aid in developing an abrasion standard for the PV industry.
Trial Implementation of a Multihazard Risk Assessment Framework for High-Impact Low-Frequency Power Grid Events
The Pacific Northwest National Laboratory developed a risk framework for modeling high-impact, low-frequency power grid events to support risk-informed decisions. In this research article, we briefly recap the framework and demonstrate its implementation for seismic and geomagnetic hazards using a benchmark reliability test system. We describe integration of a collection of models implemented to perform hazard analysis, fragility evaluation, consequence estimation and post-event restoration. We demonstrate the value of the framework as a multi-hazard power grid risk assessment and management tool. The research will benefit transmission planners and emergency planners by improving their ability to maintain a resilient grid infrastructure against impacts from major events.
Impact of Spatial Variation in Flexibility on System Operations in Electric Power Systems
With the expansion of renewable energy resources in the electric power systems, having flexibility in the setup will allow to maintain the system's reliability and prevailing operations. Such flexibility can be extracted from utility operated and/or consumer owned devices, such as, storage devices, electric vehicles, etc. For the demand side, generally consumer preferences, incentives, etc. enact on the availability of the flexibility; besides, both the spatial and temporal dimension dictates the degree of the flexibility. Consequently, the optimal dispatch of the grid resources might appear intractable as the considerable amount of flexibility are obliquely stemming from the ungovernable consumer devices. Thus characterizing the consequences of diverged feasible flexibility in the system is crucial for operations. In this paper, a procedure is developed to quantify the degree of flexibility of power systems in terms of resource dispatch reconfiguration. Specifically, we develop optimization problems to attain equivalent resource configurations for the power systems to evaluate the spatial volatility of the network and asses the flexibility of the system. The developed process is then validated using numerical simulations for IEEE-30 bus test system.
Mechanical Analysis of the Short Model Magnets for the Nb $_{3}$Sn Low-$\beta$ Quadrupole MQXF
During the development of MQXF, the new Nb 3 Sn quadrupole to be used in the large hadron collider (LHC) inner triplets for the High Luminosity upgrade, three short models were tested: MQXFS1, MQXFS3, and MQXFS5. These models differ in the use of thin or thick laminations for the iron components, in the coil design, and in the superconductive strands, rod restack process (RRP) or powder in tube (PIT). In the MQXF design, the azimuthal prestress is provided at room temperature by means of the bladder-key technology, and it is further increased during the cooldown by the differential thermal contraction of the various components. Four aluminum rods provide the longitudinal prestress. Both systems allow for a flexible control of the amount of prestress applied. As a consequence, it was possible to test the models exploring different azimuthal and longitudinal prestress conditions, in an attempt to understand their impact on the magnet performances. Here, this paper studies the mechanical behavior of these short models, also providing the strain and stresses measured by means of strain gauges installed on the aluminum shell, on the winding poles and on the rods. Finally, the paper compares the measures with the results from finite element (FE) models.
A Decision Theoretic Approach to Evaluate Radiation Detection Algorithms
There are a variety of sensor systems deployed at U.S. border crossings and ports of entry that scan for illicit nuclear material. In this work, we develop a framework for comparing the performance of detection algorithms that interpret the output of these scans and determine when secondary screening is needed. We optimize each algorithm to minimize its risk, or expected loss. We measure an algorithm’s risk by considering its performance over a sample, the probability distribution of threat sources, and the consequence of detection errors. While it is common to optimize algorithms by fixing one error rate and minimizing another, our framework allows one to simultaneously consider multiple types of detection errors. Our framework is flexible and easily adapted to many different assumptions regarding the probability of a vehicle containing illicit material, and the relative consequences of a false positive and false negative errors. Our methods can therefore inform decision makers of the algorithm family and parameter values which best reduce the threat from illicit nuclear material, given their understanding of the environment at any point in time. To illustrate the applicability of our methods, in this paper, we compare the risk from two families of detection algorithms and discuss the policy implications of our results.
Spike-Free Adaptive Sliding Mode Control: Application to Permanent Magnet Synchronous Motors
A new methodology for adaptive sliding mode control (ASMC) has been widely used to improve the control performance in various systems. This method exhibits several advantages, including low sliding mode control (SMC) chattering, no knowledge of the system disturbance bound, and no overestimation of the control gain. Despite its advantages, this method can be hampered by the spike phenomenon, slow control gain convergence, and difficulty in achieving optimal performance under varying disturbances. Consequently, this article proposes a spike-free ASMC method with a disturbance observer (DOB) to address these problems. Previous ASMC methods have been analyzed via simulations to verify the aforementioned problems. Here, this analysis highlights the need for disturbance compensation and improvements in the SMC gain adaptation law. Therefore, a DOB is designed to mitigate the spike phenomenon by compensating for disturbances. Subsequently, an SMC gain adaptation law based on disturbance error estimation is designed to eliminate the spike phenomenon completely. The proposed adaptation law makes the SMC gain to converge to a slightly higher value than the disturbance estimation error. Consequently, the proposed method not only eliminates the spike phenomenon, but also ensures optimal performance under varying disturbances. The performance of the proposed method is experimen tally validated through a comparative study.
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information
Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
Progress on the Design Development for Hard Core Components for USDA Diagnostics at ITER
An ITER hard core component (HCC) is defined for each undesirable situation with cliff edge effect consequences. Such structure system components (SCCs) are designed to prevent these situations at nuclear power plants, as well as to return and maintain a safe state in the event such a situation occurs. Only two hard core situations can really lead to cliff edge effect unless HCCs are implemented to limit the consequences: extreme earthquake (SL-3) and multiples fire in tritium building initiated by SL-3. The equipment (window and/or shutter) that closes penetrations from both sides of gallery to outside (Diagnostic building, Tritium building, and Assembly building) are classified as HCCs and SIC-2. Furthermore, design status of the HCC penetrations belong to US ITER diagnostic systems will be presented in this publication.
Seeing the forest for all the trees: Mycorrhizal‐associated nutrient economies are modulated by stem density and the synchrony between overstorey and understorey communities
1. Identifying relationships between plant communities and soil characteristics is a critical step in understanding the consequences of species gains and losses in ecosystems. The mycorrhizal associated nutrient economy (MANE) hypothesis predicts that the degree to which tree species and their mycorrhizal associates affect soil properties is driven by the relative dominance of different mycorrhizal types (often measured by relative basal area). While this approach emphasizes the importance of canopy trees, it does not account for how other factors (e.g. the density and composition of understorey trees) may alter tree–soil relationships. 2. We analysed tree–soil data from an eastern deciduous forest in southern Indiana, USA that contains >29,000 georeferenced stems, including 21 species that associate with arbuscular mycorrhizal (AM) fungi and 14 that associate with ectomycorrhizal (ECM) fungi. We sampled soils (upper 5 cm) from across the plot and modelled soil characteristics to tree communities. Given differences in soil characteristics among AM-dominated and ECM-dominated neighbourhoods, we hypothesized that relationships between tree-mycorrhizal dominance and soils would be affected by both the density of stems in the plot (owing to ‘Zinke’ individual plant effects) and composition of the understorey trees (owing to ‘trait divergence effects’) 3. In support of our Zinke hypothesis, we found that the relationships between tree-mycorrhizal dominance and soil variables (soil pH, nitrification rates and carbon to nitrogen ratio) were strengthened as stand density increased. In support of the trait divergence hypothesis, we found that as the mismatch between overstorey and understorey composition increased (e.g. AM-dominated understories beneath ECM-dominated overstories and vice versa), the relationship between tree dominance and soil variables weakened. We were able to use these insights to create mycorrhizal metrics to predict soil variables that were sensitive to the structural composition of neighbourhoods. 4. Synthesis. Our results indicate that relationships between plants and soils in forests are not only shaped by dominant overstorey trees but also the density and composition of understorey trees. Thus, efforts to predict the ecosystem consequences of species gains and losses may benefit from considering these elements of forest structure and not only the basal area of the dominant trees.
Rapid Prediction of Low (<1%) trans Fat Content in Edible Oils and Fast Food Lipid Extracts by Infrared Spectroscopy and Partial Least Squares Regression
Abstract The United States Food and Drug Administration (FDA) ruled that partially hydrogenated oils (PHO), the major dietary source of industrially produced trans fat (TF), were no longer “generally recognized as safe (GRAS)” for any use in human food. Consequently, the objective of this study was to develop a rapid screening procedure using attenuated total reflection Fourier‐transform infrared (ATR‐FTIR) spectroscopy in conjunction with partial least squares regression (PLSR) for the quantitative and accurate prediction of low concentrations of trans fatty acid (TFAs) (<1% of total fatty acids (FAs)). Broad‐based calibration models were developed for a combined set of samples consisting of edible oils and fast food lipid extracts. Predicted concentrations of TFAs in the two matrices showed good correlation with the primary reference data generated by gas chromatography (GC) ( R 2 > 0.99) and high accuracy as evidenced by low root‐mean‐square error of cross‐validation (RMSECV) values. The lowest TFA concentration, determined by GC to be 0.13% of total FAs, was accurately predicted by ATR‐FTIR/PLSR as 0.18% of total FAs. This simple, rapid ATR‐FTIR/PLSR methodology has the potential for use as a screening alternative to conventional gas chromatographic methods for predicting the TFA content of edible oils and food lipid extracts for regulatory purposes and quality control of raw material and processed food. Practical Applications F DA ruled that partially hydrogenated oils were no longer “generally recognized as safe (GRAS)” for any use in human food. Consequently, we have proposed a rapid screening procedure, based on infrared spectroscopy and chemometrics, to rapidly and accurately predict low concentrations of trans fatty acids (<1% of total fatty acids) in edible oils and food lipid extracts.
Variance components in confocal scanning laser tomography measurements of neuro‐retinal rim area and the effect of repeated measurements on the power to detect loss over time
Abstract Purpose To estimate the variation in measurements of neuro‐retinal rim area (NRA) determined by confocal scanning laser tomography and consequences for clinical follow‐up. Methods Altogether, 24 healthy subjects were randomized on –320 μ m, Moorfields and Standard NRA plane strategies. Additionally, NRA was measured in 32 glaucoma subjects. Variance components for subjects, visits and measurements were estimated with analysis of variance. Sample sizes required to detect a 6.0 × 10 −2 mm 2 NRA change were estimated assuming a significance level of 0.05 and a power of 0.8. Consequences for independent group, and paired comparison design, respectively, were analysed. Further, precision in estimates within subjects over time was investigated. Results The variation of NRA among subjects was considerably larger than the variation among visits and measurements. For glaucoma subjects, the variation among visits and measurements were of the same order but larger than in healthy subjects. It was found that independent group comparisons require inconveniently large sample sizes. Within‐subject paired comparisons over time require sample sizes of below 15 subjects. The estimated variations for glaucoma subjects imply that 54 months of follow‐up is required for detection of change from baseline. Conclusions The variance for subjects is substantial in relation to those for visits and measurements. Cross‐sectional independent group comparisons of levels of NRA are unsuitable, due to considerable subject variation. Levels of NRA differences within subjects between visits can be estimated with acceptable precision. Neuro‐retinal rim area (NRA) measurement can be used for long‐term follow‐up of glaucoma progression.
Pore-scale view of microbial turnover: combining 14 C imaging, μCT, and zymography after adding soluble carbon to soil pores of specific sizes
The location of microorganisms and substrates within soil pore networks plays a crucial role for organic carbon (C) processing, its microbial utilization and turnover, and has direct consequences for C and nutrient cycling. An optimal approach to quantify responses to new C inputs from microorganisms residing in specific pores is the addition of new C to pores of target sizes in undisturbed soil cores. We used the matric potential approach to add 14 C labeled glucose to small (< 40 μm, root free) or large (60-180 μm, potentially inhabited by roots) pores of undisturbed soil cores. Localization of glucose-derived C via 14 C imaging was related to pore size distributions and connectivity, characterized via X-ray computed micro-tomography (μCT), and to β-glucosidase activity, characterized via zymography. After two-week incubations, 1.3 times more glucose was mineralized ( 14 CO 2 ) when it was added to the large pores, however more 14 C remained in microbial biomass when glucose was added to the small pores. Consequently, while utilizing the same amounts of easily available C, the microorganisms localized in the large pores had faster turnover compared to microorganisms in small pores. Stronger associations between β-glucosidase activity and glucose-derived C were observed when glucose was added to the large pores. We conclude that 1) the matric potential approach allows placing, albeit not exactly, soluble substrates into pores of target diameter range, and 2) microorganisms localized in large pores respond to new C inputs with faster turnover, greater growth, and more intensive enzyme production compared to those inhabiting the small pores.
Crop rotational diversity enhances belowground communities and functions in an agroecosystem
Abstract Biodiversity loss, an important consequence of agricultural intensification, can lead to reductions in agroecosystem functions and services. Increasing crop diversity through rotation may alleviate these negative consequences by restoring positive aboveground–belowground interactions. Positive impacts of aboveground biodiversity on belowground communities and processes have primarily been observed in natural systems. Here, we test for the effects of increased diversity in an agroecosystem, where plant diversity is increased over time through crop rotation. As crop diversity increased from one to five species, distinct soil microbial communities were related to increases in soil aggregation, organic carbon, total nitrogen, microbial activity and decreases in the carbon‐to‐nitrogen acquiring enzyme activity ratio. This study indicates positive biodiversity–function relationships in agroecosystems, driven by interactions between rotational and microbial diversity. By increasing the quantity, quality and chemical diversity of residues, high diversity rotations can sustain soil biological communities, with positive effects on soil organic matter and soil fertility.
The functional roles of species in metacommunities, as revealed by metanetwork analyses of bird–plant frugivory networks
Abstract Understanding how biodiversity and interaction networks change across environmental gradients is a major challenge in ecology. We integrated metacommunity and metanetwork perspectives to test species’ functional roles in bird–plant frugivory interactions in a fragmented forest landscape in Southwest China, with consequences for seed dispersal. Availability of fruit resources both on and under trees created vertical feeding stratification for frugivorous birds. Bird–plant interactions involving birds feeding only on‐the‐tree or both on and under‐the‐tree (shared) had a higher centrality and contributed more to metanetwork organisation than interactions involving birds feeding only under‐the‐tree. Moreover, bird–plant interactions associated with large‐seeded plants disproportionately contributed to metanetwork organisation and centrality. Consequently, on‐the‐tree and shared birds contributed more to metanetwork organisation whereas under‐the‐tree birds were more involved in local processes. We would expect that species’ roles in the metanetwork will translate into different conservation values for maintaining functioning of seed‐dispersal networks.
Rhizosphere processes are quantitatively important components of terrestrial carbon and nutrient cycles
Abstract While there is an emerging view that roots and their associated microbes actively alter resource availability and soil organic matter ( SOM ) decomposition, the ecosystem consequences of such rhizosphere effects have rarely been quantified. Using a meta‐analysis, we show that multiple indices of microbially mediated C and nitrogen (N) cycling, including SOM decomposition, are significantly enhanced in the rhizospheres of diverse vegetation types. Then, using a numerical model that combines rhizosphere effect sizes with fine root morphology and depth distributions, we show that root‐accelerated mineralization and priming can account for up to one‐third of the total C and N mineralized in temperate forest soils. Finally, using a stoichiometrically constrained microbial decomposition model, we show that these effects can be induced by relatively modest fluxes of root‐derived C, on the order of 4% and 6% of gross and net primary production, respectively. Collectively, our results indicate that rhizosphere processes are a widespread, quantitatively important driver of SOM decomposition and nutrient release at the ecosystem scale, with potential consequences for global C stocks and vegetation feedbacks to climate.
The physics and ecology of mining carbon dioxide from the atmosphere by ecosystems
Abstract Reforesting and managing ecosystems have been proposed as ways to mitigate global warming and offset anthropogenic carbon emissions. The intent of our opinion piece is to provide a perspective on how well plants and ecosystems sequester carbon. The ability of individual plants and ecosystems to mine carbon dioxide from the atmosphere, as defined by rates and cumulative amounts, is limited by laws of physics and ecological principles. Consequently, the rates and amount of net carbon uptake are slow and low compared to the rates and amounts of carbon dioxide we release by fossil fuels combustion. Managing ecosystems to sequester carbon can also cause unintended consequences to arise. In this paper, we articulate a series of key take‐home points. First, the potential amount of carbon an ecosystem can assimilate on an annual basis scales with absorbed sunlight, which varies with latitude, leaf area index and available water. Second, efforts to improve photosynthesis will come with the cost of more respiration. Third, the rates and amount of net carbon uptake are relatively slow and low, compared to the rates and amounts and rates of carbon dioxide we release by fossil fuels combustion. Fourth, huge amounts of land area for ecosystems will be needed to be an effective carbon sink to mitigate anthropogenic carbon emissions. Fifth, the effectiveness of using this land as a carbon sink will depend on its ability to remain as a permanent carbon sink. Sixth, converting land to forests or wetlands may have unintended costs that warm the local climate, such as changing albedo, increasing surface roughness or releasing other greenhouse gases. We based our analysis on 1,163 site‐years of direct eddy covariance measurements of gross and net carbon fluxes from 155 sites across the globe.
The impacts of rising vapour pressure deficit in natural and managed ecosystems
Abstract An exponential rise in the atmospheric vapour pressure deficit (VPD) is among the most consequential impacts of climate change in terrestrial ecosystems. Rising VPD has negative and cascading effects on nearly all aspects of plant function including photosynthesis, water status, growth and survival. These responses are exacerbated by land–atmosphere interactions that couple VPD to soil water and govern the evolution of drought, affecting a range of ecosystem services including carbon uptake, biodiversity, the provisioning of water resources and crop yields. However, despite the global nature of this phenomenon, research on how to incorporate these impacts into resilient management regimes is largely in its infancy, due in part to the entanglement of VPD trends with those of other co‐evolving climate drivers. Here, we review the mechanistic bases of VPD impacts at a range of spatial scales, paying particular attention to the independent and interactive influence of VPD in the context of other environmental changes. We then evaluate the consequences of these impacts within key management contexts, including water resources, croplands, wildfire risk mitigation and management of natural grasslands and forests. We conclude with recommendations describing how management regimes could be altered to mitigate the otherwise highly deleterious consequences of rising VPD.