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Coastal dissolved oxygen (DO) concentrations have a profound impact on nearshore ecosystems and, in recent years, there has been an increased prevalance of low DO hypoxic events that negatively impact nearshore organisms. Even with advanced numerical models, accurate prediction of coastal DO variability is challenging and computationally expensive. Here, we apply machine learning techniques in order to reconstruct and predict nearshore DO concentrations in a small coastal embayment while using a comprehensive set of nearshore and offshore measurements and easily measured input (training) parameters. We show that both random forest regression (RFR) and support vector regression (SVR) models accurately reproduce both the offshore DO and nearshore DO with extremely high accuracy. In general, RFR consistently peformed slightly better than SVR, the latter of which was more difficult to tune and took longer to train. Although each of the nearshore datasets were able to accurately predict DO values using training data from the same site, the model only had moderate success when using training data from one site to predict DO at another site, which was likely due to the the complexities in the underlying dynamics across the sites. We also show that high accuracy can be achieved with relatively little training data, highlighting a potential application for correcting time series with missing DO data due to quality control or sensor issues. This work establishes the ability of machine learning models to accurately reproduce DO concentrations in both offshore and nearshore coastal waters, with important implications for the ability to detect and indirectly measure coastal hypoxic events in near real-time. Future work should explore the ability of machine learning models in order to accurately forecast hypoxic events.

Bifacial photovoltaic cell and module technology have been well understood for many years and produced in quantity by companies, such as Sanyo/Panasonic, Hitachi, PVGS, BSolar, Yingli, and LG. While manufacturers are already producing bifacial modules, there is slow adoption into the market place due to a deficiency in standardized testing and modeling techniques used to predict the performance of bifacial systems. As such, Prism Solar conducted a multi-variable, multi-site testing program to determine the impact that common installation variables have on the yearly energy output of its bifacial solar modules. The effects of the module's minimum height ratio, tilt angle, and ground albedo on the annual energy output were studied through direct field measurement. Based on these results, a best-fit model that links these variables to the annual energy output is presented. We believe this is the largest single collection of bifacial experiments presented to date, and one of the first general bifacial energy models publicly disclosed for general use. In addition, a comparison of the results of using this best-fit bifacial energy yield model derived by the authors with third-party bifacial data is also presented; this serves to validate the authors' model and adds to the utility of this paper by presenting and combining multiple sources of bifacial data.

The General Curvilinear Coastal Ocean Model (GCCOM) is a 3D curvilinear, structured-mesh, non-hydrostatic, large-eddy simulation model that is capable of running oceanic simulations. GCCOM is an inherently computationally expensive model: it uses an elliptic solver for the dynamic pressure; meter-scale simulations requiring memory footprints on the order of 10 12 cells and terabytes of output data. As a solution for parallel optimization, the Fortran-interfaced Portable−Extensible Toolkit for Scientific Computation (PETSc) library was chosen as a framework to help reduce the complexity of managing the 3D geometry, to improve parallel algorithm design, and to provide a parallelized linear system solver and preconditioner. GCCOM discretizations are based on an Arakawa-C staggered grid, and PETSc DMDA (Data Management for Distributed Arrays) objects were used to provide communication and domain ownership management of the resultant multi-dimensional arrays, while the fully curvilinear Laplacian system for pressure is solved by the PETSc linear solver routines. In this paper, the framework design and architecture are described in detail, and results are presented that demonstrate the multiscale capabilities of the model and the parallel framework to 240 cores over domains of order 10 7 total cells per variable, and the correctness and performance of the multiphysics aspects of the model for a baseline experiment stratified seamount.

Abstract Recent investigations by Castillo and Grone have led to a new method for constructing mimetic discretizations of divergence and gradient operators. Their technique, which employs a matrix formulation to incorporate mimetic constraints, is capable of producing approximations whose order at a grid boundary is equal to that in the grids interior. In this paper, we construct a second-order mimetic discretization using Castillo and Grones approach and compare it to other second-order discretizations by applying them to an elliptic boundary value problem in one dimension. A detailed perturbation analysis is provided to offer some insight into the two discretizations yielding the best numerical results in the study. [ABSTRACT FROM AUTHOR]

En este artículo, se presenta un proceso h-adaptativo que define una malla óptima para calcular la solución de problemas de contorno parabólicos usando el método de Diferencias Miméticas. La estimación del error, en la variable espacial, se hace a partir de la versión discreta del operador gradiente. La experimentación numérica evidencia el buen comportamiento del procedimiento.

We describe a technique to package 15-μm-diam single-mode fibers on a silicon substrate that can be incorporated into an endoscopic probe tip. The single-mode fibers in the array can be used in coherent imaging applications such as optical coherence tomography. Fiber-to-substrate and fiber-to-fiber coupling effects are studied using beam propagation techniques to determine the different design characteristics and the maximum length of the reduced diameter fiber that can be packaged in the probe tip. Single-mode fibers are etched to reduce the cladding diameter from 125 to 15 μm. A 2-μm-thick silica layer is grown in the silicon substrate to minimize the fiber-substrate coupling. Reduced diameter fibers are placed into a 5-mm by 150-μm trench etched in a silicon-silica substrate and fixed with UV curable cement. Active alignment is used to ensure the correct alignment of fibers. The fiber array is experimentally evaluated to test fiber placement accuracy, throughput, and cross talk. Optical coherence tomography images are also obtained with the array. [ABSTRACT FROM AUTHOR]