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The presence of POPs in the aquatic environment is not a new phenomenon; it has only become more widely evident in the last decade because continually improving chemical analysis methods have lowered the limits of detection for a wide array of chemicals tested in surface waters, sediment, and biota.
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Velocity increment probability distribution functions (PDFs) match experimental results very well for MUSCL methods, whereas WENO methods have lower intermittency.
These methods have lower NDZ and does not significantly affect the power quality of grid.
These studies show that the proposed shrinkage-based and regularization diagonal discriminant methods have lower misclassification rates than existing methods in many cases.
These methods have lower NDZ than passive methods but highly degrades the power quality of the grid.
Furthermore, the correlation-based methods have lower computational complexity than the ML methods; compare the relevant equations in Section 4. Noting these, we conclude that the correlation-based CFO estimation methods are better suited in any practical FBMC system.
The idea is to introduce sampling schemes with low number of required samples which uniquely represent the original sparse signal; these methods have lower computational complexities than the traditional techniques that employ oversampling and then apply compression.
Also, automated computation methods have lower inter- and intraobserver variations than traditional direct measurement methods.
We show that other known methods have lower classification performance than ours.
Several of the kernel methods have lower performance than our simple rule-based technique.
For simulated ER networks, the methods have lower TPR than their analysis of scale-free differential co-expression networks but also slightly lower FPR.
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