Large Input Research Articles

Metals in sediments of the Great Lakes of North America determined by factor analysis.

Metal data from eight sediment cores including 178 segments from Lakes Superior, Michigan, Huron, Erie, and Ontario were subjected to positive matrix factorization to extract characteristic loading and score plots for Pb, Cd, As, Zn, Cu, Ni, Co, and Cr. Lake Superior has significant concentrations of Cu, max 205ppm, with notable contributions of Pb, Ni, and Cr from the Precambrian bedrock in the northern part. Lake Michigan shows distinct Pb input in the southern core and likely automotive input with max during 1966-1971 in both cores. Recent input of As since 2009 is reflected in Lake Huron, max 31.4ppm, and both the western and eastern Lake Ontario, max 81ppm, which may originate in part from Lakes in the Sudbury, area and from Lake Moira both in Ontario, Canada. Highway input of metals is shown in cores from Lakes Huron, Erie, and Ontario with maxima during 1970-1973. This is supported by Zn/Pb and Zn/Cu ratios which match literature values of 6.3 and 4.7, respectively, for highway runoff. Lake Ontario has large inputs of Ni and Zn, likely from local industries. Plots of Ag vs. time for Lake Ontario point to a maximum in the eastern part in 1971, which along with literature values for Ag in Ponar Grabs of eastern Lake Ontario suggest an Ag source near Rochester, NY.

Supervised machine learning-based categorization and prediction of uranium adsorption capacity on various process parameters

Existing uranium poses significant dangers to the environment and the general population's health. Within the scope of this study, machine learning techniques were utilized to assess the characteristics that influence uranium adsorption. The adsorbent material, pH, temperature, solid-liquid ratio (SLR), and initial concentration (Ci) values were imported as the feature to evaluate using ML models in this study. The finding concluded that the Convolutional Neural Network (CNN) model performed excellently by attaining an accuracy of 98%, which is very high in classifying low and high levels of adsorption predictions. The Random Forest model was also the best performer for Qe prediction, with the lowest MSE value of 334.45 and the highest Squared-R score of 0.92 for the uranium adsorption process. Permutation Importance analysis indicated that initial concentration had the strongest impact on Qe. Knowing that a good interaction between uranyl ions and adsorbent surface would result in more active sites for trapping radioactive metal, optimizing this parameter can be quite helpful for designing new materials capable of capturing uranium species strongly from solutions. ML model excels at predicting Qe in uranium adsorption and can directly accommodate large datasets and non-linear input parameter-Qe interactions. As noted, the innovation poised to drive ML research has been made possible by recent technological developments in machine learning and can improve while offering new ways of addressing difficult environmental problems.

Numerical Analyses of Entropy Production and Thermodynamics Exergy on a Hydrogen-Fueled Micro Combustor Featuring a Diamond-Shaped Bifurcated Inner-Tube Structure for Thermophotovoltaic Applications

To improve the heat transfer mechanisms from the thermal energy to the walls, the current work presents a new structure for a micro combustor fueled by hydrogen featuring a diamond-shaped bifurcated inner-tube configuration. For this purpose, a series of three-dimensional (3D) numerical analyses are conducted to investigate the effects of the length of the diamond-shaped structure, width of inner flame channels, inlet equivalence ratio, and hydrogen volume flow rate on the key performance and thermodynamic parameters. In comparison to the conventional design, the outcomes reveal that the proposed configuration exhibits remarkable improvements in energy conversion efficiency, as it reduces the mean exhaust gas temperature by 585.98 K and boosts the exergy and radiation efficiencies by 7.78% and 14.08%, respectively. The parametric study of the design parameters indicates that elongating the diamond-shaped structure and widening the inner flame channels enhance the thermal dynamics and consequently improve the rates of heat absorption by the walls. The increase in the hydrogen volume flow rates feeds the system with additional energy and, therefore, advances the average wall temperature and its uniformity across the external surface. Nevertheless, it also reduces system efficiency due to the limited capacity of the micro combustor to utilize a large energy input along with the high magnitude of entropy production resulting particularly from the mechanism of chemical entropy generation. Operating under a stoichiometric condition balances hydrogen and oxygen in the premixed charge, achieving optimal thermal performance for the micro combustor.

A multimedia fugacity model for assessing the environmental fate of typical antibiotics in Lake Taihu with emphasis on the biomechanical characteristics of drug delivery systems

The extensive use of antibiotics in the Taihu Lake Basin has led to a significant threat to human and environmental health. In this context, the QWASI model is developed to simulate the fate of typical antibiotics in Lake Taihu. Through this model, real-time tracking of the dynamic changes in antibiotic content is possible. The study primarily focuses on evaluating the fate and transfer of antibiotics in the water and sediment phases of the lake. The model results indicate that most of the simulated concentrations and mass fluxes are within the same order of magnitude as the measured values, demonstrating a good simulation effect. However, an underestimation of the simulated output value occurs in some cases. The sediment layer serves as the main accumulation site for antibiotics, and the mass balance equation is a crucial tool for simulating the environmental distribution of antibiotics. Sulfamethoxazole (SMX) exhibits a relatively high concentration in water due to its large model input. The sensitivity analysis reveals that for ATM, SMX, and OFX, the five input parameters with the most significant impact are the half-life in water, sediment-water partition coefficient, sediment solids concentration, sediment particle density, and sediment-water diffusion MTC. For OTC, the impact of lake water depth is more prominent than the sediment-water mass transfer rate. The uncertainty analysis effectively showcases the model’s stability, with the water phase concentration showing better stability. In this process, each factor is assigned a correlation coefficient to represent its influence on the original content. The sediment phase antibiotic concentration has a relatively high uncertainty. The source intensity assessment in this study utilizes direct monitoring data of the Taihu Lake water body, ensuring higher accuracy. The major factor contributing to prediction errors is the ambiguity of the sediment phase, which is affected by numerous environmental factors. Importantly, when considering the fate of antibiotics in the lake, the biomechanical characteristics of potential drug delivery systems play a vital role. The movement and dispersion of antibiotics within the water and sediment are influenced by biomechanical forces. In the sediment, the porosity and permeability, which are related to biomechanical properties, can determine the rate at which antibiotics penetrate and accumulate. Understanding these biomechanical aspects of drug delivery systems can help in devising more effective strategies for antibiotic remediation. It can also provide insights into how the physical environment interacts with the chemical behavior of antibiotics, ultimately contributing to a more comprehensive understanding of the environmental fate of antibiotics in Lake Taihu. This is the first study to explore the fate of antibiotics in Lake Taihu and offers valuable recommendations for the restoration of antibiotic-contaminated lakes. It enriches the research perspectives on water management, especially in relation to addressing water pollution caused by antibiotic abuse.

Environmental drivers of seasonal and hourly fluxes of methane and carbon dioxide across a lowland stream network with mixed catchment

Streams serve as open windows for carbon emissions to the atmosphere due to the frequent supersaturation of carbon dioxide (CO2) and methane (CH4) that originates from large carbon input during runoff and associated in-stream processes. Due to the high spatial and temporal variability of the underlying environmental drivers (e.g., concentrations of dissolved CO2 and CH4, turbulence, and temperature), it has remained difficult to address the importance and upscale the emissions to annual whole-system and regional values. In this study, we measured concentrations and calculated emissions of CO2 and CH4 at diel and seasonal scales at 15 stations in a 1.4 km2 stream network that drains a mixed lowland catchment consisting of agriculture (210 km2), forest (56 km2), and lakes, ponds, and wetlands (22 km2) in the upper River Odense, Denmark to evaluate environmental drivers behind the spatiotemporal variability. We used automatically venting floating chambers to calculate hourly diffusive fluxes of CO2 and CH4 and CH4 ebullition. We found: 1) highly supersaturated CO2 and CH4 concentrations (median: 175 and 0.33 µmol L−1, respectively) and high diffusive fluxes of CO2 and CH4 (median: 3,608 and 19 µmol m−2 h−1, respectively); 2) lower daytime than nighttime diffusive emissions of CO2 in spring and summer, but no diel variability of CH4; 3) higher concentrations and emissions of CH4 at higher temperatures; and 4) higher emissions of CH4 at stations located in sub-catchments with higher agricultural coverage. Ebullition of CH4 peaked at two stations with soft organic sediment and low summer flow, and their ebullition alone constituted 30% of total annual CH4 emissions from the stream network. Mean annual CO2 emissions from the hydrological network (37.15 mol CO2 m−2 y−1) exceeded CH4 emissions 100-fold (0.43 mol CH4 m−2 y−1), and their combined warming potential was 1.83 kg CO2e m−2 y−1. Overall, agricultural sub-catchments had higher CH4 emissions from streams, while lakes and ponds likely reduced downstream CH4 and CO2 emissions. Our findings demonstrate that CO2 and CH4 emissions data at high spatial and temporal resolution are essential to frame the heterogeneous stream conditions, understand gas emissions regulation, and upscale to annual values for hydrological networks and larger regions.

RF photonic interference mitigation system using silicon nitride ring resonator network.

We experimentally demonstrate what we believe to be a novel RF interference mitigation technique using a network of low-loss silicon nitride ring resonators. The rings are used for complex (phase and amplitude) line-by-line shaping of higher-order sidebands from an electro-optic modulator to discriminate large and small RF signal input, thereby achieving strong (30 dB) mitigation of a large signal and virtually no mitigation for small signals.

Tail Modulo Cons, OCaml, and Relational Separation Logic

Common functional languages incentivize tail-recursive functions, as opposed to general recursive functions that consume stack space and may not scale to large inputs. This distinction occasionally requires writing functions in a tail-recursive style that may be more complex and slower than the natural, non-tail-recursive definition. This work describes our implementation of the tail modulo constructor (TMC) transformation in the OCaml compiler, an optimization that provides stack-efficiency for a larger class of functions --- tail-recursive modulo constructors --- which includes in particular the natural definition of 'List.map' and many similar recursive data-constructing functions. We prove the correctness of this program transformation in a simplified setting --- a small untyped calculus --- that captures the salient aspects of the OCaml implementation. Our proof is mechanized in the Coq proof assistant, using the Iris base logic. An independent contribution of our work is an extension of the Simuliris approach to define simulation relations that support different calling conventions. To our knowledge, this is the first use of Simuliris to prove the correctness of a compiler transformation.

Mechanisms underlying the marked enhancement of toughness in the CGHAZ of N-Ti weathering steel subjected to large heat input through the trace addition of vanadium

Mechanisms underlying the marked enhancement of toughness in the CGHAZ of N-Ti weathering steel subjected to large heat input through the trace addition of vanadium

Increased Mineral-Associated Organic Carbon and Persistent Molecules in Allochthonous Blue Carbon Ecosystems.

Coastal wetlands contain very large carbon (C) stocks-termed as blue C-and their management has emerged as a promising nature-based solution for climate adaptation and mitigation. The interactions among sources, pools, and molecular compositions of soil organic C (SOC) within blue C ecosystems (BCEs) remain elusive. Here, we explore these interactions along an 18,000 km long coastal line of salt marshes, mangroves, and seagrasses in China. We found that mineral-associated organic C (MAOC) is enriched in BCEs dominated by allochthonous inputs and abundant active minerals, leading to an increased proportion of persistent organic molecules. Specifically, soils with large allochthonous inputs (> 50%) are characterized by a substantial contribution of MAOC (> 70%) to total SOC with a notable preservation of lipids (36%) across salt marshes, mangroves, and seagrasses. The burial of allochthonous particles, derived from external sources such as rivers or tidal influxes, facilitates the formation of stable MAOC through binding to mineral surfaces or occlusion within microaggregates. The proportions of particulate organic C (POC) and MAOC are important predictors for molecular compositions of soil organic matter. Lipid proportions within molecular composition decrease as POC and autochthonous C proportions increase. These findings provide new insights into the coupled control over SOC sequestration in BCEs, emphasizing the role of allochthonous inputs, proportions of carbon pools, and persistent organic components.

The effects of variable riverine inputs and seasonal shifts in phytoplankton communities on nitrate cycling in a coastal lagoon

Estuarine systems, being situated at the interface between land and marine environments, are important sites for nitrate (NO3–) retention and processing due to large inputs, long retention time, and high biogeochemical activity. However, it remains uncertain how pelagic and benthic processes control NO3– cycling and how the relative importance of these processes is affected by seasonal changes in estuarine conditions. We measured the suite of processes governing NO3– cycling in the Curonian Lagoon (Southeast Baltic Sea) during two time periods representing spring and summer conditions. We show that in spring, benthic dissimilatory and assimilatory NO3– processes prevailed, while in summer, pelagic assimilatory processes dominated. During spring, warming temperatures and riverine nitrogen (N) inputs were associated with the onset of diatom blooms. N assimilation by diatoms resulted in the delivery of particulate organic N and organic matter to the benthos, resulting in greater denitrification in the sediments and a flux of NO3– from the water column to the sediments. In summer, phytoplankton blooms of buoyant cyanobacteria and high rates of assimilatory uptake dominated, resulting in greater particulate organic N export from the lagoon into the sea. Given the low dissolved inorganic N concentrations in summer, high uptake indicates that the pelagic community possessed a nutritional strategy to efficiently utilize multiple N forms at high rates. Overall, our findings show that diatom-dominated communities foster strong benthic-pelagic coupling, whereas cyanobacteria dominance is associated with pelagic-based N cycling. While this study sheds new light on mechanisms of NO3– retention in the Curonian Lagoon, further spatiotemporal resolution is recommended to better represent the variability in rates and to include other Baltic lagoons for a comprehensive understanding of N cycling in shallow estuarine systems.

Glacial lakes inventory and susceptibility assessment in the Alsek River Basin, Yukon, Canada

BackgroundThis study investigates glacial lake outburst floods (GLOFs) within the Alsek River Basin, Yukon, Canada, a region experiencing accelerated glacier retreat due to climate change. The formation and expansion of glacial lakes pose significant hazards to geomorphological and ecological systems, even in the absence of human infrastructure. Despite extensive research in other glaciated regions such as the Himalayas and Andes, the Canadian Cordillera remains understudied. This research aims to inventory glacial lakes and assess their susceptibility to GLOFs using remote sensing techniques and two distinct methodologies.ResultsA total of 590 glacial lakes were identified, with 57 in direct or indirect contact with glaciers, warranting a detailed susceptibility assessment. The study applied the glacier-focused methodology of Wang et al. (Mt Res Dev 31(2):122 (2011). https://doi.org/10.1659/mrd-journal-d-10-00059.1) and the lakespecific dynamics approach of Khadka et al. (Front Earth Sci 8(January):1–16 (2021). https://doi.org/10.3389/feart.2020.601288). Key findings include: High-Hazard Lakes: Lakes 22, 23, 133, 134, and 275 were consistently identified as high-hazard due to factors such as large glacier inputs, steep moraine dams, and rapid expansion rates. GLOF Events: Four GLOF events were confirmed between 2017 and 2019, with the most significant reducing Lake 21's area by over 80%. Comparative Analysis: The integration of both methodologies provided a comprehensive understanding, revealing complementary insights into glacier-driven and lake-specific GLOF triggers.ConclusionThe results underscore the critical role of glacier retreat and lake dynamics in driving GLOF hazards in the AlsekRiver Basin. The study highlights the importance of combining multiple assessment methodologies for robust hazard evaluation. Given the dynamic nature of glacial lakes and ongoing climate change, continuous monitoring and proactive hazard management strategies are essential to mitigate potential geomorphological and ecological impacts. This research contributes to the broader understanding of GLOFs in North America and underscores the need for similar assessments in other understudied glaciated regions.

Endogenous Substances Utilization for Water Self-Purification Amplification Driven by Nonexpendable H2O2 over a Micro-Potential Difference Surface.

Natural self-purification of water is limited by mass transfer processes between inert oxygen (O2) and stable pollutants. This process must rely on large energy inputs and resource consumption, which have become a global challenge in the environmental field. Here, we greatly amplify this self-purification effect of natural dissolved oxygen (DO) by nonexpendable H2O2 triggering a DRC catalyst with a micro-potential difference surface. This low-energy strategy is mainly realized by lowering the activation energy barriers of endogenous substances and simultaneously opening the mass transfer channels over the Cu-ZnO surface. In this way, pollutant electrons and energy are efficiently utilized to activate DO. Surprisingly, the rapid degradation of the pollutants is accompanied by H2O2 consumption of only 2.6% at most, sometimes even reaching zero consumption, with the instantaneous absolute amount of H2O2 exceeding 100%. The typical endocrine disruptor BPA has been proven to be harmlessly degraded to small molecule alcohols and acids by self-purification amplification, including cleavage of stable contaminants on the catalyst surface, activation of natural DO, and enhancement of mass transfer between them.

Desalination-Coupled (photo)Electrocatalysis for Production of Value-Added Chemicals

Water and energy are the most essential elements for a sustainable human society. They are strongly interdependent because energy production requires a significant amount of water, while the production, processing, distribution, and end-use of water requires large energy inputs. Several water-energy nexus technologies have emerged, including PV-electrolysis and microbial desalination. Herein, we propose a hybrid process that is driven by sunlight and boosted by electrodialysis. A sunlit inorganic photoanode initiates the desalination of saline water in the middle cell. As the chloride in the middle cell moves to the anode cell, the wastewater (WW) treatment is boosted by reactive chlorine species (RCS) generated via the reaction with photogenerated holes. An increase in the electrical conductivity in the anode cell further enhances the photoelectrocatalytic (PEC) WW treatment. Upon a potential bias, various value-added chemicals (H2 via water reduction, H2O2 via O2 reduction, and HCOOH via CO2 reduction) are produced in the cathode cell, and the production of the chemical is enhanced as the desalination proceeds because of sodium enrichment (i.e., conductivity increase). The uniqueness and advantages of this hybrid process include its broad range of operating conditions (virtually the entire pH range) and the wide variety of inorganic and organic substrates that can be treated (i.e., aquatic pollutants), while desalination boosts the overall operation and chloride catalyzes the anodic reaction, which in turn facilitates the desalination. Although tested as a proof-of-concept, the present technology opens up a novel field involving a sunlight-water-energy nexus, promising high efficiency desalination and the desalination-boosted remediation of water with simultaneous production of value-added chemicals.

(Invited) Nonlinear Frequency Response Analysis – Basics and Application for Battery Analysis

Nonlinear frequency response analysis (NFRA), also known as nonlinear impedance spectroscopy is the younger, less well-known sibling of impedance spectroscopy (EIS). In contrast to EIS, a large sinusoidal current or potential input is used; the output signal then contains higher harmonics, which are a nonlinear fingerprint of the electrochemical system under investigation. Why is this nonlinear response of practical relevance? Because electrochemical reactions have typically a nonlinear relation of current and potential. NFRA thus allows to discriminate between different reactions that give a similar linear response (i.e. EIS), to identify and parameterise reaction kinetics or surface/degradation changes, or to simply check if a process in EIS is nonlinear. Recent applications comprise batteries, electrosyntheses, and fuel cells. [1] This talk thrives to make NFRA more accessible to the electrochemistry and battery community. It introduces the NFRA technique (left Fig.) and its application to Li-ion batteries [3-6]. It gives an insight how to conduct reliable measurements, and which nonlinearities can be expected from reaction and transport processes [2]. Applied to Li-ion batteries, i.a. the potential for state-of-health estimation [3], for uniquely detecting Li-Plating (right Fig) or in-depth analysis on the impact of particle size on aging [5] is demonstrated. Models are shown to aid interpreting the NFR spectra. [6] Figure. Illustration of nonlinear frequency response analysis [2] (left) and spectra showing clear changes between plating (-10°C) vs. classical aging (25°C) [4](right).

Leveraging hierarchical structures for genetic block interaction studies using the hierarchical transformer.

1.Initially introduced in 1909 by William Bateson, classic epistasis (genetic variant interaction) refers to the phenomenon that one variant prevents another variant from a different locus from manifesting its effects. The potential effects of genetic variant interactions on complex diseases have been recognized for the past decades. Moreover, It has been studied and demonstrated that leveraging the combined SNP effects within the genetic block can significantly increase calculation power, reducing background noise, ultimately leading to novel epistasis discovery that the single SNP statistical epistasis study might overlook. However, it is still an open question how we can best combine gene structure representation modelling and interaction learning into an end-to-end model for gene interaction searching. Here, in the current study, we developed a neural genetic block interaction searching model that can effectively process large SNP chip inputs and output the potential genetic block interaction heatmap. Our model augments a previously published hierarchical transformer architecture (Liu and Lapata, 2019) with the ability to model genetic blocks. The cross-block relationship mapping was achieved via a hierarchical attention mechanism which allows the sharing of information regarding specific phenotypes, as opposed to simple unsupervised dimensionality reduction methods e.g. PCA. Results on both simulation and UK Biobank studies show our model brings substantial improvements compared to traditional exhaustive searching and neural network methods.

Can search-based testing with pareto optimization effectively cover failure-revealing test inputs?

Search-based software testing (SBST) is a widely-adopted technique for testing complex systems with large input spaces, such as Deep Learning-enabled (DL-enabled) systems. Many SBST techniques focus on Pareto-based optimization where multiple objectives are optimized in parallel to reveal failures. However, it is important to ensure that identified failures are spread throughout the entire failure-inducing area of a search domain, and not clustered in a sub-region. This ensures that identified failures are semantically diverse and reveal a wide range of underlying causes. In this paper, we present a theoretical argument explaining why testing based on Pareto optimization is inadequate for covering failure-inducing areas within a search domain. We support our argument with empirical results obtained by applying two widely used types of Pareto-based optimization techniques, namely NSGA-II (an evolutionary algorithm) and OMOPSO (a swarm-based algorithm), to two DL-enabled systems: an industrial Automated Valet Parking (AVP) system and a system for classifying handwritten digits. We measure the coverage of failure-revealing test inputs in the input space using a metric, that we refer to as the Coverage Inverted Distance (CID) quality indicator. Our results show that NSGA-II and OMOPSO are not more effective than a naïve random search baseline in covering test inputs that reveal failures. We show that this comparison remains valid for failure-inducing regions of various sizes of these two case studies. Further, we show that incorporating a diversity-focused fitness function as well as a repopulation operator in NSGA-II improves, on average, the coverage difference between NSGA-II and random search by 52.1%. However, even after diversification, NSGA-II still does not outperform random testing in covering test inputs that reveal failures. The replication package for this study is available in a GitHub repository (Replication package. https://github.com/ast-fortiss-tum/coverage-emse-24 2024.

Non-axisymmetric coupled non-stationary problem of thermoelectroelasticity for a long piezoceramic cylinder

A new closed solution to the non-axisymmetric coupled non-stationary problem of thermoelectroelasticity was constructed for a long piezoceramic cylinder for the case of satisfaction of the first and the third kind boundary conditions. Cylindrical surfaces were made as electrodes and connected to a measurement device with large input resistance. Limitation of a temperature change “load” rate made it possible to include equations of statics, electrostatics and thermal conductivity in the initial formula. The finite biorthogonal transforms are applying to explore a non-selfadjoint system of differential equations and to develop a closed solution. The obtained relations made it possible to determine the temperature and electric fields, and the stress-strain state in the piezoceramic cylinder, as well as the potential difference between cylindrical surfaces (electrodes) under non-stationary non-axisymmetric temperature impact.

Heterogeneous marine environments diversify microbial-driven polymetallic nodule formation in the South China Sea

Most studies on the genesis of polymetallic nodules suggested that nodules in the South China Sea (SCS) are hydrogenetic; however, the complexity and the heterogeneity in hydrology and geochemistry of the SCS might cause different processes of nodule formation, impacting their application and economic value. Microbial-mediated ferromanganese deposition is an important process in nodule formation, but the related microbial potentials are still unclear in the SCS. In this study, we sampled in three typical regions (A, B, and C) of the SCS enriched with polymetallic nodules. Firstly, we investigated environmental and microbial characteristics of the water columns to determine the heterogeneity of upper seawater that directly influenced deep-sea environments. Then, microbial compositions and structures in sediment cores, overlying waters, and nodules (inside and outside) collected within the same region were analyzed for inferring features of nodule environments. Microbial interactions between nodules and surrounding environments were estimated with collinear network analysis. The microbial evidence indicated that geochemical characteristics in deep sea of the SCS that were key to the polymetallic nodule formation were severely affected by organic matter flux from upper water column. The sediment in region A was sub-oxic due to the large input of terrigenous and phytoplankton-derived organic matter, potentially enhancing the overflow of reduced metals from the porewater. The intense microbial interaction between nodules and surface sediment reinforced the origin of metals for the ferromanganese deposition from the sediment (diagenetic type). Contrarily, the sediments in regions B and C were relatively rich in oxygen, and metal ions could be majorly supplied from seawater (hydrogenetic type). The large discrepancy in microbial communities between nodule inside and remaining samples suggested that nodules experienced a long-term formation process, consistent with the feature of hydrogenetic nodules. Overall, distributions and interactions of microbial communities in nodules and surrounding environments significantly contributed to the nodule formation in the SCS by manipulating biogeochemical processes that eventually determined the source and the fate of metal ions.

Perspectives on Northern Gulf of Alaska salinity field structure, freshwater pathways, and controlling mechanisms

The biologically productive Northern Gulf of Alaska (NGA) continental shelf receives large inputs of freshwater from surrounding glaciated and non-glaciated watersheds, and a better characterization of the regional salinity spatiotemporal variability is important for understanding its fate and ecological roles. We here assess synoptic to seasonal distributions of freshwater pathways of the Copper River discharge plume and the greater NGA continental shelf and slope using observations from ship-based and towed undulating conductivity-temperature-depth (CTD) instruments, satellite imagery, and satellite-tracked drifters. On the NGA continental shelf and slope we find low salinities not only nearshore but also 100–150 km from the coast (i.e. average 0–50 m salinities less than 31.9, 31.3, and 30.8 in spring, summer, and fall respectively) indicating recurring mid-shelf and shelf-break freshwater pathways. Close to the Copper River, the shelf bathymetry decouples the spreading river plume from the direct effects of seafloor-induced steering and mixing, allowing iron- and silicic acid-rich river outflow to propagate offshore within a surface-trapped plume. Self-organized mapping analysis applied to true color satellite imagery reveals common patterns of the turbid river plume. We show that the Copper River plume is sensitive to local wind forcing and exerts control over water column stratification up to ∼100 km from the river mouth. Upwelling-favorable wind stress modifies plume entrainment and density anomalies and plume width. Baroclinic transport of surface waters west of the river mouth closely follow the influence of alongshore wind stress, while baroclinic transport east of the river mouth is additionally modified by a recurring or persistent gyre. Our results provide context for considering the oceanic fate of terrestrial discharges in the Gulf of Alaska.

Three-Level Double LLC Resonant Converter with Ripple-Free Input Current for DC Microgrid Application

DC distribution systems have garnered interest in recent years due to their advantages over AC distribution systems, such as their high power conversion efficiency and lack of harmonic issues. An isolated DC-DC converter with low-input noise, high efficiency, and high-power density is required for DC microgrids within DC distribution systems. Although existing DC to DC converters have high efficiency and high power density, they still have input noise problems due to pulsating input currents. Thus, a large input filter should be inserted, which increases the cost and degrades the power density. In this study, a novel three-level double LLC resonant converter with zero-input-current ripple is presented for DC microgrid application with a high-voltage DC bus. The input-current ripple of the proposed converter theoretically decreases to zero without adding large input filters. Moreover, the voltage stress on each main switch is only half of the input voltage when using the modified three-level structure, which enables the use of low-voltage-rated power switches for high-voltage input applications. In addition, all the primary switches and secondary diodes are softly switched over a wide input voltage range. The experimental results of the prototype are presented to verify the performance of the proposed converter.