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Articles 1 - 5 of 5
Full-Text Articles in Risk Analysis
Addressing Mercury Contamination From Artisanal Gold Mining In Colombia: Pollution Exposure, Health Risk, And Educational Efforts In The Communities Of La Toma, Colombia, Kelli Mccourt
All Dissertations
Artisanal and small-scale gold mining (ASGM) has become a primary environmental and public health concern in the region of Colombia known as Alto Cauca. The predominantly Afro-descendent communities of Yolombó and La Toma in Alto Cauca have experienced pollution linked to the use of mercury in the ASGM process. For the past decade, mercury has emerged as a contaminant of increasing concern in the communities due to its toxicity and ability to bioaccumulate and biomagnify. Likewise, mining in the region raises concerns over the leaching of metals into local waters. Given the complexity of risk and pollution in the communities, …
Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin
All Dissertations
Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …
Wetland Uranium Transport Via Iron-Organic Matter Flocs And Hyporheic Exchange, Connor J. Parker
Wetland Uranium Transport Via Iron-Organic Matter Flocs And Hyporheic Exchange, Connor J. Parker
All Dissertations
Uranium (U) released from the M-Area at the Department of Energy Savannah River Site into Tims Branch, a seasonal wetland and braided stream system, is estimated to be 43,500 kg between 1965 and 1984. The motivation for this work is the uranium’s persistence in the wetland for decades, where it is estimated that 80% of the U currently remains in the Tims Branch wetland. U has begun to incorporate into wetland iron (Fe) and carbon cycles, associating with local Fe mineralogy and deposits of rich wetland organic matter (OM). The objective of this work is to characterize the chemical phases …
Seismic Performance Assessment Of Building Contents: Monetary Losses And Injuries, Sereen Majdalaweyh
Seismic Performance Assessment Of Building Contents: Monetary Losses And Injuries, Sereen Majdalaweyh
All Dissertations
Building contents include all the components that are not attached to the building which the owners place after the construction phase, such as furniture, electrical equipment, glassware, and other personal items. Loss and damage assessment of building contents proved to be challenging in performance-based earthquake engineering frameworks because of the data sparsity. Damages to building contents during an earthquake not only cause monetary losses; tumbling and over-toppling of heavy building contents could result in injuries and even deaths of occupants. While major advancements have been made in performance-based earthquake engineering; however, the focus is mainly on damages and collapse risk …
Application Of Image Processing And Convolutional Neural Networks For Flood Image Classification And Semantic Segmentation, Jaku Rabinder Rakshit Pally
Application Of Image Processing And Convolutional Neural Networks For Flood Image Classification And Semantic Segmentation, Jaku Rabinder Rakshit Pally
All Theses
Floods are among the most destructive natural hazards that affect millions of people across the world leading to severe loss of life and damage to property, critical infrastructure, and the environment. Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights and biases to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed …