Geochemical tracer data (i.e., 222Rn and four naturally occurring Ra isotopes), electromagnetic (EM) seepage meter results, and high-resolution, stationary electrical resistivity images were used to examine the bi-directional (i.e., submarine groundwater discharge and recharge) exchange of a coastal aquifer with seawater. Our study site for these experiments was Lynch Cove, the terminus of Hood Canal, WA, where fjord-like conditions dramatically limit water column circulation that can lead to recurring summer-time hypoxic events. In such a system a precise nutrient budget may be particularly sensitive to groundwater-derived nutrient loading. Shore-perpendicular time-series subsurface resistivity profiles show clear, decimeter-scale tidal modulation of the coastal aquifer in response to large, regional hydraulic gradients, hydrologically transmissive glacial terrain, and large (4-5 m) tidal amplitudes. A 5-day 222Rn time-series shows a strong inverse covariance between 222Rn activities (0.5-29 dpm L(-1)) and water level fluctuations, and provides compelling evidence for tidally modulated exchange of groundwater across the sediment/water interface. Mean Rn-derived submarine groundwater discharge (SGD) rates of 85 +/- 84 cm d(-1) agree closely in the timing and magnitude with EM seepage meter results that showed discharge during low tide and recharge during high tide events. To evaluate the importance of fresh versus saline SGD, Rn-derived SGD rates (as a proxy of total SGD) were compared to excess 226Ra-derived SGD rates (as a proxy for the saline contribution of SGD). The calculated SGD rates, which include a significant (>80%) component of recycled seawater, are used to estimate associated nutrient (NH4+, Si, PO4(3-), NO3 + NO2, TDN) loads to Lynch Cove. The dissolved inorganic nitrogen (DIN = NH4 + NO2 + NO3) SGD loading estimate of 5.9 x 10(4) mol d(-1) is 1-2 orders of magnitude larger than similar estimates derived from atmospheric deposition and surface water runoff, respectively.
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http://dx.doi.org/10.1021/es070881a | DOI Listing |
Oecologia
January 2025
Department of Oceanography, Uehiro Center for the Advancement of Oceanography, University of Hawai'i at Mānoa, Honolulu, HI, 96822, USA.
Land-based inputs, such as runoff, rivers, and submarine groundwater, can alter biologic processes on coral reefs. While the abiotic factors associated with land-based inputs have strong effects on corals, corals are also affected by biotic interactions, including other neighboring corals. The biologic responses of corals to changing environmental conditions and their neighbors are likely interactive; however, few studies address both biotic and abiotic interactions in concert.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
School of Mathematics, Renmin University of China, Beijing 100872, China.
Maximum correntropy criterion (MCC) has been an important method in machine learning and signal processing communities since it was successfully applied in various non-Gaussian noise scenarios. In comparison with the classical least squares method (LS), which takes only the second-order moment of models into consideration and belongs to the convex optimization problem, MCC captures the high-order information of models that play crucial roles in robust learning, which is usually accompanied by solving the non-convexity optimization problems. As we know, the theoretical research on convex optimizations has made significant achievements, while theoretical understandings of non-convex optimization are still far from mature.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Background: Pneumonia is an acute respiratory infection that has emerged as the predominant catalyst for escalating mortality rates worldwide. In the pursuit of the prevention and prediction of pneumonia, this work employs the development of an advanced deep-learning model by using a federated learning framework. The deep learning models rely on the utilization of a centralized system for disease prediction on the medical imaging data and pose risks of data breaches and exploitation; however, federated learning is a decentralized architecture which significantly reduces data privacy concerns.
View Article and Find Full Text PDFJ Surg Oncol
December 2024
Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
PeerJ Comput Sci
November 2024
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia.
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