Disruption Response Support For Inland Waterway Transportation

Motivated by the critical role of the inland waterways in the United States\u27 transportation system, this dissertation research focuses on pre- and post- disruption response support when the inland waterway navigation system is disrupted by a natural or manmade event. Following a comprehensive literature review, four research contributions are achieved. The first research contribution formulates and solves a cargo prioritization and terminal allocation problem (CPTAP) that minimizes total value loss of the disrupted barge cargoes on the inland waterway transportation system. It is tailored for maritime transportation stakeholders whose disaster response plans seek to mitigate negative economic and societal impacts. A genetic algorithm (GA)-based heuristic is developed and tested to solve realistically-sized instances of CPTAP. The second research contribution develops and examines a tabu search (TS) heuristic as an improved solution approach to CPTAP. Different from GA\u27s population search approach, the TS heuristic uses the local search to find improved solutions to CPTAP in less computation time. The third research contribution assesses cargo value decreasing rates (CVDRs) through a Value-focused Thinking based methodology. The CVDR is a vital parameter to the general cargo prioritization modeling as well as specifically for the CPTAP model for inland waterways developed here. The fourth research contribution develops a multi-attribute decision model based on the Analytic Hierarchy Process that integrates tangible and intangible factors in prioritizing cargo after an inland waterway disruption. This contribution allows for consideration of subjective, qualitative attributes in addition to the pure quantitative CPTAP approach explored in the first two research contributions

Anomalous Nernst Effect in Dirac Semimetal Cd3As2

Dirac and Weyl semimetals display a host of novel properties. In Cd$_3$As$_2$, the Dirac nodes lead to a protection mechanism that strongly suppresses backscattering in zero magnetic field, resulting in ultrahigh mobility ($\sim$ 10$^7$ cm$^2$ V$^{-1}$ s$^{-1}$). In applied magnetic field, an anomalous Nernst effect is predicted to arise from the Berry curvature associated with the Weyl nodes. We report observation of a large anomalous Nernst effect in Cd$_3$As$_2$. Both the anomalous Nernst signal and transport relaxation time $\tau_{tr}$ begin to increase rapidly at $\sim$ 50 K. This suggests a close relation between the protection mechanism and the anomalous Nernst effect. In a field, the quantum oscillations of bulk states display a beating effect, suggesting that the Dirac nodes split into Weyl states, allowing the Berry curvature to be observed as an anomalous Nernst effect.Comment: 13 pages, 7 figure

Electrochemical Capture of CO\u3csub\u3e2\u3c/sub\u3e from Natural Gas using a High-Temperature Ceramic-Carbonate Membrane

This study reports the first investigation of using a ceramic-carbonate dual-phase membrane to electrochemically separate CO2 from a simulated natural gas. The CO2 permeation flux density was systematically studied as a function of temperature, CO2 partial pressure and time. As expected, the flux density was observed to increase with temperature and CO2 partial pressure. Long-term stability test showed that flux density experienced an initial performance-improving “break-in” period followed by a slow decay. Post-test microstructural analysis suggested that a gradual loss of carbonate during the test could be the cause of the flux-time behavior observed

Image restoration with group sparse representation and low‐rank group residual learning

Image restoration, as a fundamental research topic of image processing, is to reconstruct the original image from degraded signal using the prior knowledge of image. Group sparse representation (GSR) is powerful for image restoration; it however often leads to undesirable sparse solutions in practice. In order to improve the quality of image restoration based on GSR, the sparsity residual model expects the representation learned from degraded images to be as close as possible to the true representation. In this article, a group residual learning based on low-rank self-representation is proposed to automatically estimate the true group sparse representation. It makes full use of the relation among patches and explores the subgroup structures within the same group, which makes the sparse residual model have better interpretation furthermore, results in high-quality restored images. Extensive experimental results on two typical image restoration tasks (image denoising and deblocking) demonstrate that the proposed algorithm outperforms many other popular or state-of-the-art image restoration methods

Stabilizing Electrochemical Carbon Capture Membrane with Al\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e3\u3c/sub\u3e Thin-Film Overcoating Synthesized by Chemical Vapor Deposition

Development of high-efficiency and cost-effective carbon capture technology is a central element of our effort to battle the global warming and climate change. Here we report that the unique high-flux and high-selectivity of electrochemical silver-carbonate dual-phase membranes can be retained for an extended period of operation by overcoating the surfaces of porous silver matrix with a uniform layer of Al2O3 thin-film derived from chemical vapor deposition