A Geophysical Survey near Blacktail Creek in the Thompson Park area near Butte, Montana

Subsurface electrical information can be obtained by electrical and self-potential methods. These geophysical methods, compared to drilling, provide a much cheaper option for investigating the hydrogeological setting. In this project report, we carried out resistivity and self-potential survey along Blacktail Creek in the Thompson Park area near Butte, Montana to understand the hydrogeological setting. Three geophysical methods have been used: 2D electrical resistivity, 3D electrical resistivity, and self-potential. The least-square inversion resistivity model results showed a general variation of resistivity with depth and delineated the extent of the ground water. The ERT results show three electrical layers, one of them is a water filled alluvial sand layer with a resistivity range of 100- 200 Ωm; the layer under the water layer is weathered granite with resistivity range of 200-800 Ωm; resistivity ranges from 800-1100 Ωm indicating granite bedrock. The survey result from the 3D resistivity profile showed relatively high resistivity in the middle of the survey area interpreted as alluvial sand. The layers above and under the middle layer have low resistant, indicating water flow and water reserves. The self-potential result indicates there is a probable downward flow ground water in the area adjacent to the stream. This downward flow was interpreted as the creek is charging ground water. Environmental managers can refer to this knowledge to have a better sense of locations with high potential to hold ground water so beaver mimicry sites can be better located to leverage water storage

Performance Analysis of l_0 Norm Constraint Least Mean Square Algorithm

As one of the recently proposed algorithms for sparse system identification, $l_0$ norm constraint Least Mean Square ($l_0$-LMS) algorithm modifies the cost function of the traditional method with a penalty of tap-weight sparsity. The performance of $l_0$-LMS is quite attractive compared with its various precursors. However, there has been no detailed study of its performance. This paper presents all-around and throughout theoretical performance analysis of $l_0$-LMS for white Gaussian input data based on some reasonable assumptions. Expressions for steady-state mean square deviation (MSD) are derived and discussed with respect to algorithm parameters and system sparsity. The parameter selection rule is established for achieving the best performance. Approximated with Taylor series, the instantaneous behavior is also derived. In addition, the relationship between $l_0$-LMS and some previous arts and the sufficient conditions for $l_0$-LMS to accelerate convergence are set up. Finally, all of the theoretical results are compared with simulations and are shown to agree well in a large range of parameter setting.Comment: 31 pages, 8 figure

Space-Time Transmit-Receive Design for Colocated MIMO Radar

This chapter deals with the design of multiple input multiple-output (MIMO) radar space-time transmit code (STTC) and space-time receive filter (STRF) to enhance moving targets detection in the presence of signal-dependent interferences, where we assume that some knowledge of target and clutter statistics are available for MIMO radar system according to a cognitive paradigm by using a site-specific (possible dynamic) environment database. Thus, an iterative sequential optimization algorithm with ensuring the convergence is proposed to maximize the signal to interference plus noise ratio (SINR) under the similarity and constant modulus constraints on the probing waveform. In particular, each iteration of the proposed algorithm requires to solve the hidden convex problems. The computational complexity is linear with the number of iterations and polynomial with the sizes of the STTW and the STRF. Finally, the gain and the computation time of the proposed algorithm also compared with the available methods are evaluated

Skin Ageing and Cancer

Human matrix metalloproteinases (MMPs) belong to the M10 family of the MA clan of endopeptidases. They are ubiquitarian enzymes, structurally characterized by an active site where a Zn2+ atom, coordinated by three histidines, plays the catalytic role, assisted by a glutamic acid as a general base. Based on their structure and substrate specificity, they can be categorized into five main subgroups, namely (1) collagenases (MMP-1, MMP-8 and MMP-13); (2) gelatinases (MMP-2 and MMP-9); (3) stromelysins (MMP-3, MMP-10 and MMP-11); (4) matrilysins (MMP-7 and MMP-26) and (5) membrane-type (MT) MMPs (MMP-14, MMP-15, MMP-16, MMP-17, MMP-24 and MMP-25). MMPs can act on extracellular matrix (ECM) and non-ECM components affecting degradation and modulation of the ECM, growth-factor activation and cell-cell and cell-matrix signalling. In skin, MMPs are secreted by different cell types such as fibroblasts, keratinocytes, macrophages, endothelial cells, mast cells, and eosinophils. This chapter reviews the role of MMPs in maintaining skin homeostasis, skin ageing and skin cancer