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The materials obtained from the biomimetic method have superior biocompatibility/bioactivity compared to the material synthesized by the wet chemical precipitation method with an incubation period of 24 h.
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The proposed method has superior memory efficiency.
The computational results compared with other published results show that our method has superior performance.
In addition, the proposed method has superior accuracy to the convergent method described in the IMO guidelines.
It is shown that the first method has superior performance in the sense of gain and efficiency.
The proposed method has superior computational accuracy compared with the common approach of approximating curved boundaries with stair cases.
We introduce the classic source iteration method, compare it with the new splitting method and prove the splitting method has superior convergence properties.
Computer simulations indicated that the GMDR method has superior performance in its ability to identify epistatic loci, compared with current methods in the literature.
Finally, we demonstrate that our finite element method has superior stability, accuracy, and computational efficiency over several boundary element algorithms that have previously appeared in the literature.
Finally, a series of contrast experiments with comprehensive analysis demonstrate that the proposed fusion method has superior performances in terms of image quality and fusion effect compared to several existing algorithms.
From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in enhancing the rail defect AE signal and reducing the strong background noise, offering an effective multiresolution approach for rail defect detection under high-speed and strong-noise condition.
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