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  • 1
    Publication Date: 2022-10-27
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kalra, T. S., Li, X., Warner, J. C., Geyer, W. R., & Wu, H. Comparison of physical to numerical mixing with different tracer advection schemes in estuarine environments. Journal of Marine Science and Engineering, 7(10), (2019): 338, doi: 10.3390/jmse7100338.
    Description: The numerical simulation of estuarine dynamics requires accurate prediction for the transport of tracers, such as temperature and salinity. During the simulation of these processes, all the numerical models introduce two kinds of tracer mixing: (1) by parameterizing the tracer eddy diffusivity through turbulence models leading to a source of physical mixing and (2) discretization of the tracer advection term that leads to numerical mixing. Physical and numerical mixing both vary with the choice of horizontal advection schemes, grid resolution, and time step. By simulating four idealized cases, this study compares the physical and numerical mixing for three different tracer advection schemes. Idealized domains only involving physical and numerical mixing are used to verify the implementation of mixing terms by equating them to total tracer variance. Among the three horizontal advection schemes, the scheme that causes the least numerical mixing while maintaining a sharp front also results in larger physical mixing. Instantaneous spatial comparison of mixing components shows that physical mixing is dominant in regions of large vertical gradients, while numerical mixing dominates at sharp fronts that contain large horizontal tracer gradients. In the case of estuaries, numerical mixing might locally dominate over physical mixing; however, the amount of volume integrated numerical mixing through the domain compared to integrated physical mixing remains relatively small for this particular modeling system.
    Description: This study was funded through the Coastal Model Applications and Field Measurements Project and the Cross-shore and Inlets Project, US Geological Survey Coastal Marine Hazards and Resources Program.
    Keywords: Physical mixing ; Numerical mixing ; Advection schemes ; Estuarine mixing
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2019
    Description: Currently, black-odor river has received great attention in China. In this study, the micro-nano bubble technology (MBT) was used to mitigate the water pollution rapidly and continuously by increasing the concentration of dissolved oxygen (DO) in water. During treatment, the concentration of DO increased from 0.60 mg/L to over 5.00 mg/L, and the oxidation reduction potential (ORP) also changed from a negative value to over 100.00 mV after only five days aeration. High throughput pyrosequencing technology was employed to identify the microbial community structure. At genus level, the dominant bacteria were anaerobic and nutrient-loving microbes (e.g., Arcobacter sp., Azonexus sp., and Citrobacter sp.) before, and the relative abundances of aerobic and functional microbes (e.g., Perlucidibaca sp., Pseudarcicella sp., Rhodoluna sp., and Sediminibacterium sp.) were increased after treatment. Meanwhile, the water quality was significantly improved with about 50% removal ratios of chemical oxygen demand (CODCr) and ammonia nitrogen (NH4+-N). Canonical correspondence analysis (CCA) results showed that microbial community structure shaped by COD, DO, NH4+-N, and TP, CCA1 and CCA2 explained 41.94% and 24.56% of total variances, respectively. Overall, the MBT could improve the water quality of urban black-odor river by raising the DO and activate the aerobic microbes.
    Electronic ISSN: 2073-4441
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by MDPI
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  • 3
    Publication Date: 2019
    Description: Homogeneous image change detection research has been well developed, and many methods have been proposed. However, change detection between heterogeneous images is challenging since heterogeneous images are in different domains. Therefore, direct heterogeneous image comparison in the way that we do it is difficult. In this paper, a method for heterogeneous synthetic aperture radar (SAR) image and optical image change detection is proposed, which is based on a pixel-level mapping method and a capsule network with a deep structure. The mapping method proposed transforms an image from one feature space to another feature space. Then, the images can be compared directly in a similarly transformed space. In the mapping process, some image blocks in unchanged areas are selected, and these blocks are only a small part of the image. Then, the weighted parameters are acquired by calculating the Euclidean distances between the pixel to be transformed and the pixels in these blocks. The Euclidean distance calculated according to the weighted coordinates is taken as the pixel gray value in another feature space. The other image is transformed in a similar manner. In the transformed feature space, these images are compared, and the fusion of the two different images is achieved. The two experimental images are input to a capsule network, which has a deep structure. The image fusion result is taken as the training labels. The training samples are selected according to the ratio of the center pixel label and its neighboring pixels’ labels. The capsule network can improve the detection result and suppress noise. Experiments on remote sensing datasets show the final detection results, and the proposed method obtains a satisfactory performance.
    Electronic ISSN: 2072-4292
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by MDPI
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  • 4
    Publication Date: 2019
    Description: Impervious surfaces (IS) coverage is a quantifiable environmental indicator for understanding urban sprawl and its potential impacts on sustainability of urban ecological environments. Numerous studies have previously demonstrated global and regional IS variation, but little attention has been paid to the different internal and external patterns of IS development as urbanization progresses. This study estimates IS coverage in a subtropical coastal area of Xiamen, southeastern China, from Landsat TM/OLI images obtained in 1994, 2000, 2004, 2010, and 2015, and quantifies its spatio–temporal variations using IS change trajectories and radar graphs. During the study period, IS gradually expanded along the shoreline in a pattern resembling the shape of the bay. The land surfaces are classified into four zones: IS1 and IS2, dominated by cultivated land and forest; IS3, complex land use/coverage; and IS4, built-up areas. The progression and transformations of these zones highlight the main trends in IS changes in the study area. The trajectories of the zones form a layered structure in which the urban centers of each district progressively gain IS4, and transformations into IS3 and IS2 extend successively beyond the centers. The orientation of IS expansion in each of the six districts of Xiamen is revealed by radar graphs. The areas containing intermediate and high percentages IS each expanded in generally consistent directions throughout the study period, except in Tong’an district, which showed a change in the direction of expansion of its area of intermediate and high IS.
    Electronic ISSN: 2071-1050
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by MDPI
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  • 5
    Publication Date: 2018
    Description: Considering the high random and non-static property of the rainfall-runoff process, lots of models are being developed in order to learn about such a complex phenomenon. Recently, Machine learning techniques such as the Artificial Neural Network (ANN) and other networks have been extensively used by hydrologists for rainfall-runoff modelling as well as for other fields of hydrology. However, deep learning methods such as the state-of-the-art for LSTM networks are little studied in hydrological sequence time-series predictions. We deployed ANN and LSTM network models for simulating the rainfall-runoff process based on flood events from 1971 to 2013 in Fen River basin monitored through 14 rainfall stations and one hydrologic station in the catchment. The experimental data were from 98 rainfall-runoff events in this period. In between 86 rainfall-runoff events were used as training set, and the rest were used as test set. The results show that the two networks are all suitable for rainfall-runoff models and better than conceptual and physical based models. LSTM models outperform the ANN models with the values of R 2 and N S E beyond 0.9, respectively. Considering different lead time modelling the LSTM model is also more stable than ANN model holding better simulation performance. The special units of forget gate makes LSTM model better simulation and more intelligent than ANN model. In this study, we want to propose new data-driven methods for flood forecasting.
    Electronic ISSN: 2073-4441
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by MDPI
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  • 6
    Publication Date: 2019
    Description: Mineral deposits concealed by thick cover sequences present special problems for geochemical exploration. A variety of penetrating geochemical methods have been developed in the last few decades to explore for buried deposits. The theoretical basis of the mechanism by which metals migrate upward from buried deposits through the cover to the surface is still not fully understood. One hypothesis is that metal particles or metal elements could be carried onto bubbles or micro-flow of geogas and migrate upward to the surface. After years of study, nano-scale metal-bearing particles have been widely observed in geogas samples from different kinds of concealed deposits. However, the occurrence of these metal-bearing particles carried by geogases in near-surface media, such as soil, has not been studied in detail. In this study, metal-bearing nanoparticles were observed in samples from soils and fault gouges over the Shenjiayao gold deposit. The results indicate that (1) the ore-forming elements in soils can only come from deep-seated ore bodies and they occur in nanoparticles in the study area; (2) there is an obvious relationship between metal nanoparticles in fault gouges and soils; (3) the metallic nanoparticles in fault gouges represent a transitional phase along the whole vertical migration process. In addition, the observation results show that the metal-bearing nanoparticles tend to be adsorbed on the surface of clay minerals, which provide theoretical support for using fine fraction soils as sampling media to carry out geochemical exploration in sediment-covered terrains. Based on the results and discussion, a simple migration model was built in this paper.
    Electronic ISSN: 2075-163X
    Topics: Geosciences
    Published by MDPI
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  • 7
    Publication Date: 2019
    Description: The idea of Ubiquitous Power Internet of Things (UPIoTs) accelerates the development of intelligent monitoring and diagnostic technologies. In this paper, a diagnostic method suitable for power equipment in an interference environment was proposed based on the deep Convolutional Neural Network (CNN): MobileNet-V2 and Digital Image Processing (DIP) methods to conduct fault identification process: including fault type classification and fault localization. A data visualization theory was put forward in this paper, which was applied in frequency response (FR) curves of transformer to obtain dataset. After the image augmentation process, the dataset was input into the deep CNN: MobileNet-V2 for training procedures. Then a spatial-probabilistic mapping relationship was established based on traditional Frequency Response Analysis (FRA) fault diagnostic method. Each image in the dataset was compared with the fingerprint values to get traditional diagnosing results. Next, the anti-interference abilities of the proposed CNN-DIP method were compared with that of the traditional one while the magnitude of the interference gradually increased. Finally, the fault tolerance of the proposed method was verified by further analyzing the deviations between the wrong diagnosing results with the corresponding actual labels. Experimental results showed that the proposed deep visual identification (CNN-DIP) method has a higher diagnosing accuracy, a stronger anti-interference ability and a better fault tolerance.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by MDPI
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  • 8
    Publication Date: 2017-06-20
    Description: Taxi trajectories reflect human mobility over the urban roads’ network. Although taxi drivers cruise the same city streets, there is an observed variation in their daily profit. To reveal the reasons behind this issue, this study introduces a novel approach for investigating and understanding the impact of human mobility patterns (taxi drivers’ behavior) on daily drivers’ profit. Firstly, a K-means clustering method is adopted to group taxi drivers into three profitability groups according to their driving duration, driving distance and income. Secondly, the cruising trips and stopping spots for each profitability group are extracted. Thirdly, a comparison among the profitability groups in terms of spatial and temporal patterns on cruising trips and stopping spots is carried out. The comparison applied various methods including the mash map matching method and DBSCAN clustering method. Finally, an overall analysis of the results is discussed in detail. The results show that there is a significant relationship between human mobility patterns and taxi drivers’ profitability. High profitability drivers based on their experience earn more compared to other driver groups, as they know which places are more active to cruise and to stop and at what times. This study provides suggestions and insights for taxi companies and taxi drivers in order to increase their daily income and to enhance the efficiency of the taxi industry.
    Electronic ISSN: 2078-2489
    Topics: Computer Science
    Published by MDPI
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