Geochemical processes in deep water sediment cores from eastern Lake Erie.

Heavy metal pollution is particularly significant in Lake Erie. Sediments act as both sink and source for metal contaminants. Sediment core samples were collected in May and June, 2004, from the eastern basin of Lake Erie in order to assess the mobility of heavy metals in benthic ecosystems. Five extractions were applied to solid phase analysis including acetic acid, ascorbic acid, sodium dithionite, nitric acid/oxalic acid and total digestion. Pore water and solid data revealed that mobility of heavy metals was influenced by redox reactions. Manganese, Fe and S were the crucial elements for Eh profiles. Distributions of trace metals were affected by the oxidization and reduction of Mn and Fe. Three zones were identified by the profiles of dissolved Mn and Fe: Mn oxidation, Mn reduction and Fe reduction. Cadmium had high mobility in the Mn oxide zone while Ni and Co were released with reducible Mn. Solid extractions indicated that anthropogenic pollution has improved in Lake Erie; reactive Mn and Fe oxides remobilize trace metals; the distribution of trace metal oxides are associated with the oxides of Fe, Al and Si. (Abstract shortened by UMI.)Dept. of Earth Sciences. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .S655. Source: Masters Abstracts International, Volume: 45-01, page: 0253. Thesis (M.Sc.)--University of Windsor (Canada), 2006

Investigation of LDA+U and hybrid functional methods on the description of the electronic structure of YTiO3 under high pressure

Currently, there are two main methodologies for the calculation of the electronic structure and properties of crystalline solids. Known as the Hartree-Fock Method (HF) and the Density Functional Theory (DFT) methods, they are based on two different theories for the numerical solution of the many electron Schrödinger equation. Unfortunately, in highly correlated electron systems like transition metal complexes, both the HF and DFT methods have severe shortcomings. In some cases they fail to provide the correct description of the electronic structure. In general, the HF method overestimates the energy band gap due to the neglect of electron correlation effects and the incorrect description of electron interactions in the unoccupied orbitals. In contrast, even though electron correlation effects are implicitly included in the density functional, DFT often underestimates the band gap due to the improper treatment of the electron self-interaction. To amend these problems, two approaches have been proposed. The deficiency in the HF scheme can be corrected using a hybrid method which adds exchange correlation energy borrowed from DFT to help reduce the band gap energy and bring the predictions in better agreement with experiment. To improve DFT, the LDA+U approach, which uses a model Hubbard-like Hamiltonian including an on-site repulsion parameter U, can be employed. This method is a convenient semi-quantitative way to efficiently calculate the band gap of insulators and semiconductors. In this thesis, the electronic structure of YTiO3 under pressure is investigated using the aforementioned approaches. The performance and reliability of these methods will be examined, compared and discussed

HIGH PERFORMANCE AND ULTRA HIGH PERFORMANCE CONCRETE WITH LOCALLY AVAILABLE MATERIALS FROM SASKATCHEWAN

Reinforced concrete structures exhibit various durability problems, such as the corrosion of reinforcing steel, sulfate attack, etc., when exposed to harsh environments. This type of damage often leads to very serious technical and economic problems, such as a short lifetime of infrastructure and high costs associated with their long term maintenance and repair. High performance concrete (HPC) and ultra-high performance concrete (UHPC) could play key roles in solving or in mitigating these problems. The main research goal of this thesis was to determine whether it is possible to produce high performance concrete (HPC), very-high performance concrete (VHPC) and ultra-high performance concrete (UHPC) that have unique combinations of strength, freeze-thaw durability and self-placeability at competitive costs using materials locally available in Saskatchewan. To develop HPC and VHPC/UHPC, a statistical experimental design was used to perform experimental designs, analyze the fitting models and optimize multiple responses. The procedure was implemented using the Design-Expert Version 9.0 software. Seven materials were researched in this project to make concrete, namely: water, cement, silica fume, silica flour, fine sand, steel fiber, and superplasticizer (SP). Four different properties were measured, including the compressive strength, splitting tensile strength, air content of hardened concrete and flow cone test. After analyzing the results of these tests, it was found that the goal of developing a HPC material with the specified properties was achieved (flow cone spread value = 274 mm and, after 28 days, the obtained properties were: compressive strength = 82 MPa, splitting tensile strength = 23 MPa and air content = 6%.). The goal of making VHPC with the specified properties was obtained (flow cone spread value = 274 mm and, after 28 days, the obtained properties were: compressive strength iv = 102.4 MPa and splitting tensile strength = 23 MPa) regardless of air content. Nevertheless, the results of the analysis clearly showed that it would be impossible to produce a UHPC with a 28 day compressive strength greater than 150 MPa using the mix ingredients and fabrication processes adopted in this study

Sketch-a-Net that Beats Humans

We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result of explicitly embedding the unique characteristics of sketches in our model: (i) a network architecture designed for sketch rather than natural photo statistics, (ii) a multi-channel generalisation that encodes sequential ordering in the sketching process, and (iii) a multi-scale network ensemble with joint Bayesian fusion that accounts for the different levels of abstraction exhibited in free-hand sketches. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photo or sketch. Our network on the other hand not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral

Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval

In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognizes two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended, that consists of 330,000 sketches and 204,000 photos spanning across 110 categories. Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic. We then formulate a ZS-SBIR framework to jointly model sketches and photos into a common embedding space. A novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap. External semantic knowledge is further embedded to aid semantic transfer. We show that, rather surprisingly, retrieval performance significantly outperforms that of state-of-the-art on existing datasets that can already be achieved using a reduced version of our model. We further demonstrate the superior performance of our full model by comparing with a number of alternatives on the newly proposed dataset. The new dataset, plus all training and testing code of our model, will be publicly released to facilitate future researchComment: Oral paper in CVPR 201