bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mesoscale Effects on Carbon Export: A Global Perspective
Authors: Harrison Cheryl S. , Long Matthew C. , Lovenduski Nicole S. , Moore Jefferson K. 
Published: February 2018

Carbon export from the surface to the deep ocean is a primary control on global carbon budgets and is mediated by plankton that are sensitive to physical forcing. Earth system models generally do not resolve ocean mesoscale circulation (
(10–100) km), scales that strongly affect transport of nutrients and plankton. The role of mesoscale circulation in modulating export is evaluated by comparing global ocean simulations conducted at 1° and 0.1° horizontal resolution. Mesoscale resolution produces a small reduction in globally integrated export production (<2%); however, the impact on local export production can be large (±50%), with compensating effects in different ocean basins. With mesoscale resolution, improved representation of coastal jets block off‐shelf transport, leading to lower export in regions where shelf‐derived nutrients fuel production. Export is further reduced in these regions by resolution of mesoscale turbulence, which restricts the spatial area of production. Maximum mixed layer depths are narrower and deeper across the Subantarctic at higher resolution, driving locally stronger nutrient entrainment and enhanced summer export production. In energetic regions with seasonal blooms, such as the Subantarctic and North Pacific, internally generated mesoscale variability drives substantial interannual variation in local export production. These results suggest that biogeochemical tracer dynamics show different sensitivities to transport biases than temperature and salinity, which should be considered in the formulation and validation of physical parameterizations. Efforts to compare estimates of export production from observations and models should account for large variability in space and time expected for regions strongly affected by mesoscale circulation.
Faults Get Colder Through Transient Granular Vortices
Authors: Einav I. , Rognon P. , Miller T. , Sulem J. 
Published: February 2018

AbstractFault temperatures govern their weakening and control the dynamics of earthquakes during slip. Despite predictions of significant temperature rise within fault gouges during earthquake events, observations of frictional melting zones along exhumed faults are relatively rare. Could there be a heat transfer mechanism, previously not considered, that results in ubiquitously colder faults during earthquakes? We demonstrate that the remarkable, previously neglected mechanism of heat transfer through transient granular vortices may be at the core of this. We present and analyze results from perpetual simple shear experiments on a system of granular disks with which we are able to quantify the sizes and lifetimes of granular vortices within fault gouges during earthquakes. We then develop a formula that captures the contribution these vortices have on heat transfer. Using this formula, we show that crustal faults such as those in the San Andreas system may experience a maximum temperature rise 5 to 10 times lower than previously thought.
New Mass‐Conserving Bedrock Topography for Pine Island Glacier Impacts Simulated Decadal Rates of Mass Loss
Authors: Nias I. J. , Cornford S. L. , Payne A. J. 
Published: February 2018

AbstractHigh‐resolution ice flow modeling requires bedrock elevation and ice thickness data, consistent with one another and with modeled physics. Previous studies have shown that gridded ice thickness products that rely on standard interpolation techniques (such as Bedmap2) can be inconsistent with the conservation of mass, given observed velocity, surface elevation change, and surface mass balance, for example, near the grounding line of Pine Island Glacier, West Antarctica. Using the BISICLES ice flow model, we compare results of simulations using both Bedmap2 bedrock and thickness data, and a new interpolation method that respects mass conservation. We find that simulations using the new geometry result in higher sea level contribution than Bedmap2 and reveal decadal‐scale trends in the ice stream dynamics. We test the impact of several sliding laws and find that it is at least as important to accurately represent the bedrock and initial ice thickness as the choice of sliding law.
Linking Low‐Frequency Large‐Scale Circulation Patterns to Cold Air Outbreak Formation in the Northeastern North Atlantic
Authors: Papritz L. , Grams C. M. 
Published: February 2018

AbstractThe regional variability of wintertime marine cold air outbreaks (CAOs) in the northeastern North Atlantic is studied focusing on the role of weather regimes in modulating the large‐scale circulation. Each regime is characterized by a typical CAO frequency anomaly pattern and a corresponding imprint in air‐sea heat fluxes. Cyclonically dominated regimes, Greenland blocking and the Atlantic ridge regime are found to provide favorable conditions for CAO formation in at least one major sea of the study region; CAO occurrence is suppressed, however, by blocked regimes whose associated anticyclones are centered over northern Europe (European / Scandinavian blocking). Kinematic trajectories reveal that strength and location of the storm tracks are closely linked to the pathways of CAO air masses and, thus, CAO occurrence. Finally, CAO frequencies are also linked to the strength of the stratospheric polar vortex, which is understood in terms of associated variations in the frequency of weather regimes.
Estimation of Antarctic Land‐Fast Sea Ice Algal Biomass and Snow Thickness From Under‐Ice Radiance Spectra in Two Contrasting Areas
Authors: Wongpan P. , Meiners K. M. , Langhorne P. J. , Heil P. , Smith I. J. , Leonard G. H. , Massom R. A. , Clementson L. A. , Haskell T. G. 
Published: February 2018

AbstractFast ice is an important component of Antarctic coastal marine ecosystems, providing a prolific habitat for ice algal communities. This work examines the relationships between normalized difference indices (NDI) calculated from under‐ice radiance measurements and sea ice algal biomass and snow thickness for Antarctic fast ice. While this technique has been calibrated to assess biomass in Arctic fast ice and pack ice, as well as Antarctic pack ice, relationships are currently lacking for Antarctic fast ice characterized by bottom ice algae communities with high algal biomass. We analyze measurements along transects at two contrasting Antarctic fast ice sites in terms of platelet ice presence: near and distant from an ice shelf, i.e., in McMurdo Sound and off Davis Station, respectively. Snow and ice thickness, and ice salinity and temperature measurements support our paired in situ optical and biological measurements. Analyses show that NDI wavelength pairs near the first chlorophyll a (chl a) absorption peak (≈440 nm) explain up to 70% of the total variability in algal biomass. Eighty‐eight percent of snow thickness variability is explained using an NDI with a wavelength pair of 648 and 567 nm. Accounting for pigment packaging effects by including the ratio of chl a‐specific absorption coefficients improved the NDI‐based algal biomass estimation only slightly. Our new observation‐based algorithms can be used to estimate Antarctic fast ice algal biomass and snow thickness noninvasively, for example, by using moored sensors (time series) or mapping their spatial distributions using underwater vehicles.
The Wintertime Covariation of CO2 and Criteria Pollutants in an Urban Valley of the Western United States
Authors: Bares Ryan , Lin John C. , Hoch Sebastian W. , Baasandorj Munkhbayar , Mendoza Daniel L. , Fasoli Ben , Mitchell Logan , Catharine Douglas , Stephens Britton B. 
Published: February 2018

AbstractNumerous mountain valleys experience wintertime particulate pollution events, when persistent cold air pools (PCAPs) develop and inhibit atmospheric mixing, leading to the accumulation of pollutants. Here we examine the relationships between trace gases and criteria pollutants during winter in Utah's Salt Lake Valley, in an effort to better understand the roles of transport versus chemical processes during differing meteorological conditions as well as insights into how targeted reductions in greenhouse gases will impact local air quality in varying meteorological conditions. CO2 is a chemically inert gas that is coemitted during fossil fuel combustion with pollutants. Many of these coemitted pollutants are precursors that react chemically to form secondary particulate matter. Thus, CO2 can serve as a stable tracer and potentially help distinguish transport versus chemical influences on pollutants. During the winter of 2015–2016, we isolated enhancements in CO2 over baseline levels due to urban emissions (“CO2ex”). CO2ex was paired with similar excesses in other pollutant concentrations. These relationships were examined during different wintertime conditions and stages of pollution episodes: (a) Non‐PCAP, (b) beginning, and (c) latter stages of an episode. We found that CO2ex is a good indicator of the presence of gaseous criteria pollutants and a reasonable indicator of PM2.5. Additionally, the relationships between CO2ex and criteria pollutants differ during different phases of PCAP events which provide insight into meteorological and transport processes. Lastly, we found a slight overestimation of CO:CO2 emission ratios and a considerable overestimation of NOx:CO2 by existing inventories for the Salt Lake Valley.
An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions
Authors: Zhang Jiangjiang , Lin Guang , Li Weixuan , Wu Laosheng , Zeng Lingzao 
Published: February 2018

AbstractEnsemble smoother (ES) has been widely used in inverse modeling of hydrologic systems. However, for problems where the distribution of model parameters is multimodal, using ES directly would be problematic. One popular solution is to use a clustering algorithm to identify each mode and update the clusters with ES separately. However, this strategy may not be very efficient when the dimension of parameter space is high or the number of modes is large. Alternatively, we propose in this paper a very simple and efficient algorithm, i.e., the iterative local updating ensemble smoother (ILUES), to explore multimodal distributions of model parameters in nonlinear hydrologic systems. The ILUES algorithm works by updating local ensembles of each sample with ES to explore possible multimodal distributions. To achieve satisfactory data matches in nonlinear problems, we adopt an iterative form of ES to assimilate the measurements multiple times. Numerical cases involving nonlinearity and multimodality are tested to illustrate the performance of the proposed method. It is shown that overall the ILUES algorithm can well quantify the parametric uncertainties of complex hydrologic models, no matter whether the multimodal distribution exists.
Impacts of Near‐Term Climate Change on Irrigation Demands and Crop Yields in the Columbia River Basin
Authors: Rajagopalan K. , Chinnayakanahalli K. J. , Stockle C. O. , Nelson R. L. , Kruger C. E. , Brady M. P. , Malek K. , Dinesh S. T. , Barber M. E. , Hamlet A. F. , Yorgey G. G. , Adam J. C. 
Published: February 2018

AbstractAdaptation to a changing climate is critical to address future global food and water security challenges. While these challenges are global, successful adaptation strategies are often generated at regional scales; therefore, regional‐scale studies are critical to inform adaptation decision making. While climate change affects both water supply and demand, water demand is relatively understudied, especially at regional scales. The goal of this work is to address this gap, and characterize the direct impacts of near‐term (for the 2030s) climate change and elevated CO2 levels on regional‐scale crop yields and irrigation demands for the Columbia River basin (CRB). This question is addressed through a coupled crop‐hydrology model that accounts for site‐specific and crop‐specific characteristics that control regional‐scale response to climate change. The overall near‐term outlook for agricultural production in the CRB is largely positive, with yield increases for most crops and small overall increases in irrigation demand. However, there are crop‐specific and location‐specific negative impacts as well, and the aggregate regional response of irrigation demands to climate change is highly sensitive to the spatial crop mix. Low‐value pasture/hay varieties of crops—typically not considered in climate change assessments—play a significant role in determining the regional response of irrigation demands to climate change, and thus cannot be overlooked. While, the overall near‐term outlook for agriculture in the region is largely positive, there may be potential for a negative outlook further into the future, and it is important to consider this in long‐term planning.
A Primer for Model Selection: The Decisive Role of Model Complexity
Authors: Höge Marvin , Wöhling Thomas , Nowak Wolfgang 
Published: February 2018

AbstractSelecting a “best” model among several competing candidate models poses an often encountered problem in water resources modeling (and other disciplines which employ models). For a modeler, the best model fulfills a certain purpose best (e.g., flood prediction), which is typically assessed by comparing model simulations to data (e.g., stream flow). Model selection methods find the “best” trade‐off between good fit with data and model complexity. In this context, the interpretations of model complexity implied by different model selection methods are crucial, because they represent different underlying goals of modeling. Over the last decades, numerous model selection criteria have been proposed, but modelers who primarily want to apply a model selection criterion often face a lack of guidance for choosing the right criterion that matches their goal. We propose a classification scheme for model selection criteria that helps to find the right criterion for a specific goal, i.e., which employs the correct complexity interpretation. We identify four model selection classes which seek to achieve high predictive density, low predictive error, high model probability, or shortest compression of data. These goals can be achieved by following either nonconsistent or consistent model selection and by either incorporating a Bayesian parameter prior or not. We allocate commonly used criteria to these four classes, analyze how they represent model complexity and what this means for the model selection task. Finally, we provide guidance on choosing the right type of criteria for specific model selection tasks. (A quick guide through all key points is given at the end of the introduction.)
Flexible Fiber and Fabric Batteries
Authors: Zhang Chenjun , Zhu Jixin , Lin Huijuan , Huang Wei 
Published: February 2018

AbstractThe rapid development of wearable electronics requires developing flexible and efficient energy storage systems. To this end, novel flexible fiber and fabric batteries attract increasing attention due to their combined superiorities in flexibility, weavability, and miniaturization compared with conventional bulky structures. Herein, the recent progress in the electrode fabrication, structure design, and battery performance of fiber and fabric batteries is summarized, and the possibility of battery textiles, challenges, and future directions to satisfy the requirement of the next‐generation flexible energy storage systems is also highlighted.
Tunable Antireflection Coating to Remove Index‐Matching Requirement for Interference Lithography
Authors: Bacon‐Brown Daniel A. , Braun Paul V. 
Published: February 2018

AbstractInterference lithography is a flexible technique for creating 3D periodic nano‐ and microstructures that can be used to make a wide variety of crystal lattices, but as it is found, some lattices require index‐matched substrates to eliminate reflections at the photoresist–substrate interface. In this study a tunable‐refractive index quarter wavelength‐thickness polystyrene/poly(vinyl methyl ether) homopolymer blend backside antireflection coating, which alleviates this issue, is presented. The coating's refractive index can be tuned from 1.47 to 1.6, drastically reducing reflections at the photoresist–substrate interface for substrates with refractive indices as low as 1.35 for normal incidence and even lower for angled illumination. By injecting the light through the substrate and applying the antireflection layer to the top of the photoresist, interference lithography can even be performed on high refractive index substrates, such as indium tin oxide (ITO)‐coated glass. Fabrication of hexagonal, face‐centered cubic, and simple cubic lattices in SU‐8 photoresist (refractive index of 1.59) is demonstrated using 532 nm laser light on nonindex matched substrates including ITO‐coated glass and borosilicate glass, and the effects of reflection interference on the photonic bandstructure are investigated.
The Effects of Nanoscale Zerovalent Iron on Microbial Fuel Cells in the Start‐up Process
Authors: Xiao Xiaofeng , Wang Qingping , Jin Xiaoying , Chen Zuliang 
Published: February 2018

AbstractMunicipal sludge contains carbon‐rich material that can be used in microbial fuel cells (MFCs) to generate electrical power. However, manufactured nanoparticles found in sewage pose a potential risk in MFCs although the extent of this is still unclear. In this study, MFCs using sludge containing two types of nanoscale zerovalent iron (nZVI) as substrates are investigated. It is found that in the MFC start‐up process, the maximum output voltage of the green synthesized nZVI (g‐nZVI) group and chemically synthesized nZVI group (c‐nZVI) decreases to 202.5 mV and 94.7 mV, respectively. Meanwhile the maximum output voltage of the control group is higher than 500 mV. These outcomes are attributed to a high concentration of nZVI being inhibited during the start‐up phase, and nZVI makes no essentially significant change in generating electricity despite MFCs being started successfully. Concerning the results on the rate of removing chemical oxygen demand, scanning electron microscope, and electrochemical characterization indicate that green synthesized nZVI, compared to chemically synthesized nZVI, reveals less antibacterial activity to microbial community in anode. The electronic properties of MFCs are lightly affected due to the mature biofilm adhering to the anode's electrode surface. Finally, nZVI can effectively extend and stabilize the output voltage of the produced electricity by releasing Fe3+ ions.
2D Black Phosphorus: from Preparation to Applications for Electrochemical Energy Storage
Authors: Wu Shuxing , Hui Kwan San , Hui Kwun Nam 
Published: February 2018

AbstractBlack phosphorus (BP) is rediscovered as a 2D layered material. Since its first isolation in 2014, 2D BP has triggered tremendous interest in the fields of condensed matter physics, chemistry, and materials science. Given its unique puckered monolayer geometry, 2D BP displays many unprecedented properties and is being explored for use in numerous applications. The flexibility, large surface area, and good electric conductivity of 2D BP make it a promising electrode material for electrochemical energy storage devices (EESDs). Here, the experimental and theoretical progress of 2D BP is presented on the basis of its preparation methods. The structural and physiochemical properties, air instability, passivation, and EESD applications of 2D BP are discussed systemically. Specifically, the latest research findings on utilizing 2D BP in EESDs, such as lithium‐ion batteries, supercapacitors, and emerging technologies (lithium–sulfur batteries, magnesium‐ion batteries, and sodium‐ion batteries), are summarized. On the basis of the current progress, a few personal perspectives on the existing challenges and future research directions in this developing field are provided.
Brief Report: Association of Quantitative and Topographic Assessment of Heberden's Nodes With Knee Osteoarthritis: Data From the Osteoarthritis Initiative
Authors: Kumar Neil M. , Hafezi‐Nejad Nima , Guermazi Ali , Haj‐Mirzaian Arya , Haugen Ida K. , Roemer Frank W. , Demehri Shadpour 
Published: February 2018

ObjectiveTo determine whether the presence, number, and topography (digit location and symmetry) of Heberden's nodes are associated with the incidence and progression of radiographic osteoarthritis (OA) of the knee.MethodsWe analyzed 8,023 knees (with 8 years of follow‐up) from the Osteoarthritis Initiative. Cox regression was performed on Heberden's node presence, total number, location, and symmetry (using 2 symmetry index models) obtained at baseline physical examination as well as self‐report of Heberden's node presence for evaluation of association with radiographic knee OA incidence (development of a Kellgren/Lawrence grade of ≥2) and progression (worsening in the medial joint space narrowing score of ≥1). Covariate adjustments relevant to OA outcomes were performed.ResultsThe presence of Heberden's nodes (in 64% of the subjects) at baseline physical examinations, but not subjective self‐report of Heberden's nodes, was associated with radiographic knee OA incidence (hazard ratio [HR] 1.19 and 95% confidence interval [95% CI] 1.001–1.402 [approached statistical significance]). Each additional Heberden's node found on physical examination was associated with knee OA incidence (HR 1.03 [95% CI 1.000–1.054] [approached statistical significance]) and progression (HR 1.04 [95% CI 1.016–1.063]). Knee OA incidence and progression were associated with Heberden's nodes located on the third digit (HR 1.26 [95% CI 1.068–1.487] and 1.18 [95% CI 1.019–1.361], respectively) and first digit (HR 1.186 [95% CI 0.992–1.418] [approached statistical significance] and HR 1.26 [95% CI 1.084–1.453], respectively). Heberden's node symmetry was associated with knee OA incidence (model 1 HR 1.09 [95% CI 0.997–1.185] [approached statistical significance]) and progression (model 2 HR 1.13 [95% CI 1.035–1.234]).ConclusionThe number of Heberden's nodes, their locations, and symmetry were associated with knee OA incidence and progression over 8 years.
Expression of miR‐634 in gastric carcinoma and its effects on proliferation, migration, and invasion of gastric cancer cells
Authors: Guo Jiao , Zhang Chun‐Dong , An Jia‐Xiang , Xiao Yun‐Yun , Shao Shuai , Zhou Nuo‐Ming , Dai Dong‐Qiu 
Published: February 2018

AbstractThis study aims to observe the expression of microRNA (miR)‐634 in different gastric cancer cell lines and tissues, and to study the effects of miR‐634 on the proliferation, migration, and invasion of the gastric cancer cells. The miR‐634 mimics and miR‐634 inhibitors were transfected by lentivirus into human gastric cancer SGC‐7901 and MGC‐803 cells, and the miR‐634 cells without transfection were used as the control group (NC group). The expression of miR‐634 in the transfected cells was detected by qRT‐PCR. Cell viability was measured by the CCK8 assay. The migration and invasion ability of the cells were detected by scratch assays and Transwell® chamber assays, respectively, and the luciferase assay verified the binding of miR‐634 to the target gene JAG1. The expression level of miR‐634 in gastric cancer tissues and cell lines was significantly lower than that in normal adjacent tissues and control cells. The survival of cells was significantly decreased, and number of cells migrating and invading was decreased in the miR‐634 mimics group. However, in the miR‐634 inhibitor group, the opposite results were observed. Over‐expression of miR‐634 inhibited the proliferation, migration, and invasion of gastric cancer cell lines, and the miR‐634 target gene was JAG1.