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Late Holocene history of Tokotan Lake (Kuril Archipelago, Russian Far East): The use of lacustrine records for paleoclimatic reconstructions from geologically dynamic settings
A high-resolution, multiproxy analysis of a core from Tokotan Lake, Urup Island, illustrates the complex paleoenvironmental signals that can be preserved in sedimentary deposits from geologically active areas, such as the Kuril Archipelago. Diatom and geochemical analyses of the Tokotan core suggest a shallowing and lessening of the lake area over the last ~3450 cal BP. Without other proxies, the record could be interpreted as showing a general decrease in precipitation over the Late Holocene. However, palynological data indicate no change in vegetation, thereby implying no significant climatic variation. Neither of these paleoclimatic interpretations agrees with Late Holocene reconstructions from the Kuril Archipelago or the Russian mainland. Paleomagnetic data imply variable sediment sources during the lake's history, indicating alterations within the Tokotan catchment (e.g., shifts in gradients or sediment loads in the inflowing streams). Such changes would be particularly important in the Shabalina River, which today has formed a partial dam across the lake's outlet. Here, the results of the Tokotan study indicate that other factors (e.g., volcanism, seismic activity, sea-level changes) rather than climate were more probable determinants of the Late Holocene lake record. Given the geologic dynamism of the region, it is perhaps not surprising that paleo-records from the Kuril Islands display an inconsistency in the numbers and/or duration of proposed Holocene climatic oscillations, underscoring the importance of multiproxy investigations in areas that are or have been geologically dynamic.
Identifying and quantifying a complete set of full-power initiating events during early design stages of high-temperature gas-cooled reactors
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Scripta Viewpoint Set: Materials science aspects related to additive manufacturing
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Viewpoint Set on Nuclear Materials Science
Materials science has historically played critical roles in developing today’s nuclear energy systems. The current mainstream nuclear materials such as the zirconium alloys for fuels and core components and the reactor pressure vessel (RPV) steels were essential enabling materials for present day nuclear power and they undergo continued science-based fine tuning. Discovery of void swelling and other radiation effect phenomena in a fast neutron spectrum is largely responsible for the development of modern nuclear steels with enhanced radiation tolerance and, to a certain extent, the struggle of fast spectrum reactor development. Understandings the effects of neutron irradiation and the operating thermochemical environment in heat-resistant alloys, core graphite, and SiC-coated particle fuels have enabled the high temperature helium cooled reactors as a passively safe nuclear energy technology that is now anticipating mass deployment.
Robust adaptive beamforming against large steering vector mismatch using multiple uncertainty sets
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Setting boundaries on the recipe for a successful RbF post-deposition treatment of CIGS
RbF post-deposition treatments have been explored in the literature for increasing the open-circuit voltage, fill factor, and hence the efficiency of Cu(In,Ga)Se 2 solar cells. However, given the few papers documenting the experimental steps, it was difficult to quickly reproduce the results. This contribution describes some of the optimization steps that led to a successful RbF PDT based on device performance. Here we present results that put boundaries on the temperatures of the RbF cell and the lamp (for sample heating) setpoint. The best recipe for our specific growth process is documented in detail so that others may copy the procedure and possibly arrive at a successful RbF PDT in a reasonable time.
Effect of monomer structure on ionic conductivity in a systematic set of polyester electrolytes
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Regulating TNCs: Should Uber and Lyft set their own rules?
We evaluate the impact of three proposed regulations of transportation network companies (TNCs) like Uber, Lyft and Didi: (1) A minimum wage for drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip congestion tax. The impact is assessed using a queuing theoretic equilibrium model which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. We show that a floor placed under driver earnings can push the ride-hailing platform to hire more drivers and offer more rides, at the same time that passengers enjoy faster rides and lower total cost, while platform rents are reduced. Contrary to standard competitive labor market theory, enforcing a minimum wage for drivers benefits both drivers and passengers, and promotes the efficiency of the entire system. This surprising outcome holds for almost all model parameters, and it occurs because the wage floor curbs TNC labor market power. In contrast to a wage floor, imposing a cap on the number of vehicles hurts drivers, because the platform reaps all the benefits of limiting supply. The congestion tax has the expected impact: fares increase, wages and platform revenue decrease. Here, we also construct variants of the model to briefly discuss platform subsidy, platform competition, and autonomous vehicles.
Lamb waves and electro-mechanical impedance based damage detection using a mobile PZT transducer set
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A basis set for exploration of sensitivity to prescribed ocean conditions for estimating human contributions to extreme weather in CAM5.1-1degree
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The influence of environmental microseismicity on detection and interpretation of small-magnitude events in a polar glacier setting
Glacial environments exhibit temporally variable microseismicity. To investigate how microseismicity influences event detection, we implement two noise-adaptive digital power detectors to process seismic data from Taylor Glacier, Antarctica. We add scaled icequake waveforms to the original data stream, run detectors on the hybrid data stream to estimate reliable detection magnitudes and compare analytical magnitudes predicted from an ice crack source model. We find that detection capability is influenced by environmental microseismicity for seismic events with source size comparable to thermal penetration depths. When event counts and minimum detectable event sizes change in the same direction (i.e. increase in event counts and minimum detectable event size), we interpret measured seismicity changes as ‘true’ seismicity changes rather than as changes in detection. Generally, one detector (two degree of freedom (2dof)) outperforms the other: it identifies more events, a more prominent summertime diurnal signal and maintains a higher detection capability. We conclude that real physical processes are responsible for the summertime diurnal inter-detector difference. One detector (3dof) identifies this process as environmental microseismicity; the other detector (2dof) identifies it as elevated waveform activity. Our analysis provides an example for minimizing detection biases and estimating source sizes when interpreting temporal seismicity patterns to better infer glacial seismogenic processes.
Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE
Imaging N-glycan spatial distribution in tissues using mass spectrometry imaging (MSI) is emerging as a promising tool in biological and clinical applications. However, there is currently no high throughput tool for visualization and molecular annotation of N-glycans in MSI data, which significantly slows down data processing and hampers the applicability of this approach. In this work, we present how METASPACE, an open-source cloud engine for molecular annotation of MSI data, can be used to automatically annotate, visualize, analyze, and interpret high-resolution mass spectrometry-based spatial N-glycomics data. METASPACE is an emerging tool in spatial metabolomics, but the lack of compatible glycan databases has limited its application for comprehensive N-glycan annotations from MSI datasets. We created NGlycDB, a public database of N-glycans, by adapting available glycan databases. We demonstrate the applicability of NGlycDB in METASPACE by analyzing MALDI-MSI data from formalin-fixed paraffin-embedded (FFPE) human kidney and mouse lung tissue sections. We added NGlycDB to METASPACE for public use, thus facilitating applications of MSI in glycobiology.
A Comprehensive Set of High-Quality Point Charges for Simulations of Metal–Organic Frameworks
Most classical simulations of metal-organic frameworks model electrostatic interactions using point charges on each atom in the structure. We report atomic point charges derived from periodic DFT electronic structure calculations for over 2,000 unique experimentally synthesized MOFs. These charges are publicly available as a supplement to the Computation-Ready Experimental MOF database. These DFT derived atomic point charges are compared to semi-empirical group contribution and charge equilibration methods for assigning charges. As an example of using these charges, we examined each MOF for usefulness in the adsorptive removal of tert-butyl mercaptan (TBM) from natural gas. Monte Carlo simulations revealed many candidate MOF structures with high selectivity for TBM over CH 4 and high TBM capacity. As a result, we anticipate that this public dataset of atomic point charges for MOFs will facilitate high-throughput screening for a wide variety applications where electrostatic interactions must be considered.
Setting the System Boundaries of “Energy for Water” for Integrated Modeling
Many studies over the last two decades have addressed the “water-energy nexus,” generally defined as the inter-dependency between water and energy in their supply, processing, distribution, and use. The research community currently disaggregates the water-energy nexus into two components: “water for energy” and “energy for water.” While there seems to be clear consensus on the definition of “water for energy”—that is, water required for the extraction, processing, and transformation of energy as well as the irrigation of bioenergy—there has been less agreement on the definition and system boundaries of “energy for water.” Here we represent six integrated assessment modeling teams presently incorporating the hydrologic system and water demands into existing global models of energy, agriculture, land use, and climate. In this article, we propose system boundaries of “energy for water” that are appropriate for integrated energy and water modeling, and introduce a third category of processes that are relevant for the water-energy nexus, but that are not logically classified as either “water for energy” or “energy for water.”
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