1,396 research outputs found

    Research on Noise Removal in Fiber Grating Sensing Signal

    Get PDF

    Effectiveness of variable-width buffer design for sediment reduction

    Get PDF
    Vegetative buffer strips are vegetated areas (usually strips) between fields and waterbodies that can mitigate the effects of agricultural activities by acting as a physical barrier to sediment and nutrients being carried to streams, and thereby have been widely used as a best management practice (BMP). Buffers can slow surface water flow and allow greater water infiltration, trapping the sediment entering from cultivated areas. In addition, research has documented the ability of buffers to removing pollutants from surface runoff and/or shallow groundwater (Lin et al., 2007; Ryder and Fares, 2008). For example, nitrogen can be removed by plant uptake or by microorganism denitrification within buffers (Lowrance and Hubbard, 2001)

    Impacts of Extreme Precipitation Events on Performance of Conservation Practices

    Get PDF
    Climate-induced changes in the volume and erosive power of precipitation is the most important effect of global climate change on soil erosion and surface runoff (Nearing, 2001). Greater frequency and intensity of extreme weather events have been observed in the last decades due to the climate change (Milly et al., 2002; SWCS, 2003). One of the direct consequences of those extreme events on agricultural land is the acceleration of topsoil loss, which leads to soil degradation and pollutant transport from the field. A linearly increase of the amount of daily precipitation by 5% or 10% could increase soil erosion by 10.7% and 35.6%, respectively (Savabi et al., 1993). In addition, the risk of gully erosion and stream channel erosion are also increased during the extreme events. Consequently, a more severe and lasting damage to soil and water resources can be caused from these forms of erosion, which require more intensive and costly conservation treatments (SWCS, 2003)

    Protein structure similarity from principle component correlation analysis

    Get PDF
    BACKGROUND: Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities. RESULTS: We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC) analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins. CONCLUSION: The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum eigenvalues can be highly effective in clustering structurally or topologically similar proteins. We believe that the PCC analysis of interaction matrix is highly flexible in adopting various structural parameters for protein structure comparison
    corecore