Introduction to Library Trends 55 (3) Winter 2007: Libraries in Times of War, Revolution and Social Change

published or submitted for publicatio

Impact of pressure dissipation on fluid injection into layered aquifers

Carbon dioxide (CO2) capture and subsurface storage is one method for reducing anthropogenic CO2 emissions to mitigate climate change. It is well known that large-scale fluid injection into the subsurface leads to a buildup in pressure that gradually spreads and dissipates through lateral and vertical migration of water. This dissipation can have an important feedback on the shape of the CO2 plume during injection, and the impact of vertical pressure dissipation, in particular, remains poorly understood. Here, we investigate the impact of lateral and vertical pressure dissipation on the injection of CO2 into a layered aquifer system. We develop a compressible, two-phase model that couples pressure dissipation to the propagation of a CO2 gravity current. We show that our vertically integrated, sharp-interface model is capable of efficiently and accurately capturing water migration in a layered aquifer system with an arbitrary number of aquifers. We identify two limiting cases --- `no leakage' and `strong leakage' --- in which we derive analytical expressions for the water pressure field for the corresponding single-phase injection problem. We demonstrate that pressure dissipation acts to suppress the formation of an advancing CO2 tongue during injection, resulting in a plume with a reduced lateral extent. The properties of the seals and the number of aquifers determine the strength of pressure dissipation and subsequent coupling with the CO2 plume. The impact of pressure dissipation on the shape of the CO2 plume is likely to be important for storage efficiency and security

Rethinking Sanitation: Lessons and Innovation for Sustainability and Success in the New Millennium

human development, water, sanitation

An optimal factor analysis approach to improve the wavelet-based image resolution enhancement techniques

The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods

Decuplet baryon magnetic moments in a QCD-based quark model beyond quenched approximation

We study the decuplet baryon magnetic moments in a QCD-based quark model beyond quenched approximation. Our approach for unquenching the theory is based on the heavy baryon perturbation theory in which the axial couplings for baryon - meson and the meson-meson-photon couplings from the chiral perturbation theory are used together with the QM moment couplings. It also involves the introduction of a form factor characterizing the structure of baryons considered as composite particles. Using the parameters obtained from fitting the octet baryon magnetic moments, we predict the decuplet baryon magnetic moments. The $\Omega^-$ magnetic moment is found to be in good agreement with experiment: $\mu_{\Omega^-}$ is predicted to be $-1.97 \mu_N$ compared to the experimental result of ($-$2.02 $\pm$ 0.05) $\mu_N$.Comment: 19 pages, 2 figure

Quality-based Multimodal Classification Using Tree-Structured Sparsity

Recent studies have demonstrated advantages of information fusion based on sparsity models for multimodal classification. Among several sparsity models, tree-structured sparsity provides a flexible framework for extraction of cross-correlated information from different sources and for enforcing group sparsity at multiple granularities. However, the existing algorithm only solves an approximated version of the cost functional and the resulting solution is not necessarily sparse at group levels. This paper reformulates the tree-structured sparse model for multimodal classification task. An accelerated proximal algorithm is proposed to solve the optimization problem, which is an efficient tool for feature-level fusion among either homogeneous or heterogeneous sources of information. In addition, a (fuzzy-set-theoretic) possibilistic scheme is proposed to weight the available modalities, based on their respective reliability, in a joint optimization problem for finding the sparsity codes. This approach provides a general framework for quality-based fusion that offers added robustness to several sparsity-based multimodal classification algorithms. To demonstrate their efficacy, the proposed methods are evaluated on three different applications - multiview face recognition, multimodal face recognition, and target classification.Comment: To Appear in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014