Metals can be processed to reach ultra-high strength, but usually at a drastic loss of ductility. Here, we review recent advances in overcoming this tradeoff, by purposely deploying heterogeneous ...nanostructures in an otherwise single-phase metal. Several structural designs are being explored, including bimodal, harmonic, lamellar, gradient, domain-dispersed, and hierarchical nanostructures. These seemingly distinct tactics share a unifying design principle in that the intentional structural heterogeneities induce non-homogeneous plastic deformation, and the nanometer-scale features dictate steep strain gradients, thereby enhancing strain hardening and consequently uniform tensile ductility at high flow stresses. Moreover, these heterogeneous nanostructures in metals play a role similar to multiple phases in complex alloys, functionally graded materials and composites, sharing common material design and mechanics principles. Our review advocates this broad vision to help guide future innovations towards a synergy between high strength and high ductility, through highlighting several recent designs as well as identifying outstanding challenges and opportunities.
Mixed noise (such as Gaussian, impulse, stripe, and deadline noises) contamination is a common phenomenon in hyperspectral imagery (HSI), greatly degrading visual quality and affecting subsequent ...processing accuracy. By encoding sparse prior to the spatial or spectral difference images, total variation (TV) regularization is an efficient tool for removing the noises. However, the previous TV term cannot maintain the shared group sparsity pattern of the spatial difference images of different spectral bands. To address this issue, this article proposes a group sparsity regularization of the spatial difference images for HSI restoration. Instead of using <inline-formula> <tex-math notation="LaTeX">\ell _{1} </tex-math></inline-formula>- or <inline-formula> <tex-math notation="LaTeX">\ell _{2} </tex-math></inline-formula>-norm (sparsity) on the difference image itself, we introduce a weighted <inline-formula> <tex-math notation="LaTeX">\ell _{2,1} </tex-math></inline-formula>-norm to constrain the spatial difference image cube, efficiently exploring the shared group sparse pattern. Moreover, we employ the well-known low-rank Tucker decomposition to capture the global spatial-spectral correlation from three HSI dimensions. To summarize, a weighted group sparsity-regularized low-rank tensor decomposition (LRTDGS) method is presented for HSI restoration. An efficient augmented Lagrange multiplier algorithm is employed to solve the LRTDGS model. The superiority of this method for HSI restoration is demonstrated by a series of experimental results from both simulated and real data, as compared with the other state-of-the-art TV-regularized low-rank matrix/tensor decomposition methods.
Exfoliating montmorillonite (Mt) to nanolayers is a crucial step during producing clay/polymer nanocomposites(CPN). Only well-exfoliated and well-dispersed Mt. nanolayers in the polymer matrix can ...significantly improve the properties of the nanocomposites. This review examines the latest scientific advances in the exfoliation methods of Mt., the insights into the exfoliation mechanisms, and the peculiar functionalities of the resultant CPN. The direct exfoliation of Mt. dispersed in water or organic solvents is often intensified by ultrasonication. Grinding of Mt. in the form of solid in a high-energy ball mill can directly exfoliate Mt. to some extent. Exfoliating Mt. for producing CPN is mainly achieved through so-called in situ exfoliation, solution exfoliation and melt exfoliation. The Mt./polymer nanocomposites exhibit typically improved barrier properties, mechanical strength, thermal stability, and fire retardancy. The literature survey suggests that future work should place emphases on developing green and effective exfoliation methods, and deepening understanding of exfoliation mechanisms and the interfacial interactions between the inorganic Mt. nanolayers and organic monomers/polymers. Future research is suggested to assembling exfoliated Mt. nanolayers with functional polymeric molecules or other nano-scale building blocks to produce functional hierarchical nanomaterials with practical applications.
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•Literature survey and opinions on exfoliating montmorillonite (Mt) into nanolayers.•Organic modification of Mt for exfoliating Mt in polymer matrix.•Exfoliation of Mt by in situ polymerization, in polymer solution, and melt exfoliation•Exfoliated Mt for improving barrier, mechanical and thermal properties, and flame retardancy.•Remarks on challenges and opportunities in exfoliation of Mt for nanocomposites.
Rain streaks removal is an important issue in outdoor vision systems and has recently been investigated extensively. In this paper, we propose a novel video rain streak removal approach FastDeRain, ...which fully considers the discriminative characteristics of rain streaks and the clean video in the gradient domain. Specifically, on the one hand, rain streaks are sparse and smooth along the direction of the raindrops, whereas on the other hand, clean videos exhibit piecewise smoothness along the rain-perpendicular direction and continuity along the temporal direction. Theses smoothness and continuity result in the sparse distribution in the different directional gradient domain. Thus, we minimize: 1) the 4 norm to enhance the sparsity of the underlying rain streaks; 2) two l1 norm of unidirectional total variation regularizers to guarantee the anisotropic spatial smoothness; and 3) an 4 norm of the time-directional difference operator to characterize the temporal continuity. A split augmented Lagrangian shrinkage algorithm-based algorithm is designed to solve the proposed minimization model. Experiments conducted on synthetic and real data demonstrate the effectiveness and efficiency of the proposed method. According to the comprehensive quantitative performance measures, our approach outperforms other state-of-the-art methods, especially on account of the running time. The code of FastDeRain can be downloaded at https://github.com/TaiXiangJiang/FastDeRain.
Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, nonlocal low-rank tensor approximation-based denoising methods have attracted much ...attention due to their advantage of being capable of fully exploiting the nonlocal self-similarity and global spectral correlation. Existing nonlocal low-rank tensor approximation methods were mainly based on two common decomposition Tucker or CANDECOMP/PARAFAC (CP) methods and achieved the state-of-the-art results, but they are subject to certain issues and do not produce the best approximation for a tensor. For example, the number of parameters for Tucker decomposition increases exponentially according to its dimensions, and CP decomposition cannot better preserve the intrinsic correlation of the HSI. In this article, a novel nonlocal tensor-ring (TR) approximation is proposed for HSI denoising by using TR decomposition to explore the nonlocal self-similarity and global spectral correlation simultaneously. TR decomposition approximates a high-order tensor as a sequence of cyclically contracted third-order tensors, which has strong ability to explore these two intrinsic priors and to improve the HSI denoising results. Moreover, an efficient proximal alternating minimization algorithm is developed to optimize the proposed TR decomposition model efficiently. Extensive experiments on three simulated data sets under several noise levels and two real data sets verify that the proposed TR model provides better HSI denoising results than several state-of-the-art methods in terms of quantitative and visual performance evaluations.
Hyperspectral unmixing has attracted much attention in recent years. Single sparse unmixing assumes that a pixel in a hyperspectral image consists of a relatively small number of spectral signatures ...from large, ever-growing, and available spectral libraries. Joint-sparsity (or row-sparsity) model typically enforces all pixels in a neighborhood to share the same set of spectral signatures. The two sparse models are widely used in the literature. In this paper, we propose a joint-sparsity-blocks model for abundance estimation problem. Namely, the abundance matrix of size <inline-formula> <tex-math notation="LaTeX">m\times n </tex-math></inline-formula> is partitioned to have one row block and <inline-formula> <tex-math notation="LaTeX">s </tex-math></inline-formula> column blocks and each column block itself is joint-sparse. It generalizes both the single (i.e., <inline-formula> <tex-math notation="LaTeX">s=n </tex-math></inline-formula>) and the joint (i.e., <inline-formula> <tex-math notation="LaTeX">s=1 </tex-math></inline-formula>) sparsities. Moreover, concatenating the proposed joint-sparsity-blocks structure and low rankness assumption on the abundance coefficients, we develop a new algorithm called joint-sparse-blocks and low-rank unmixing . In particular, for the joint-sparse-blocks regression problem, we develop a two-level reweighting strategy to enhance the sparsity along the rows within each block. Simulated and real-data experiments demonstrate the effectiveness of the proposed algorithm.
As a preprocessing step, hyperspectral image (HSI) restoration plays a critical role in many subsequent applications. Recently, based on the framework of subspace representation and low-rank ...matrix/tensor factorization (LRMF/LRTF), many single-factor-regularized methods add various regularizations on the spatial factor to characterize its spatial prior knowledge. However, these methods neglect the common characteristics among different bands and the spectral continuity of HSIs. To tackle this issue, this article establishes a bridge between the factor-based regularization and the HSI priors and proposes a double-factor-regularized LRTF model for HSI mixed noise removal. The proposed model employs LRTF to characterize the spectral global low rankness, introduces a weighted group sparsity constraint on the spatial difference images (SpatDIs) of the spatial factor to promote the group sparsity in the SpatDIs of HSIs, and suggests a continuity constraint on the spectral factor to promote the spectral continuity of HSIs. Moreover, we develop a proximal alternating minimization-based algorithm to solve the proposed model. Extensive experiments conducted on the simulated and real HSIs demonstrate that the proposed method has superior performance on mixed noise removal compared with the state-of-the-art methods based on subspace representation, noise modeling, and LRMF/LRTF.
Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of ...multitemporal remotely sensed data. This paper aims at reconstructing the missing information by a nonlocal low-rank tensor completion method. First, nonlocal correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then, low rankness of the identified fourth-order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared with other patch-based methods such as the recently proposed patch matching-based multitemporal group sparse representation method.
Hyperspectral image (HSI) mixed noise removal is a fundamental problem and an important preprocessing step in remote sensing fields. The low-rank approximation-based methods have been verified ...effective to encode the global spectral correlation for HSI denoising. However, due to the large scale and complexity of real HSI, previous low-rank HSI denoising techniques encounter several problems, including coarse rank approximation (such as nuclear norm), the high computational cost of singular value decomposition (SVD) (such as Schatten <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula>-norm), and adaptive rank selection (such as low-rank factorization). In this article, two novel factor group sparsity-regularized nonconvex low-rank approximation (FGSLR) methods are introduced for HSI denoising, which can simultaneously overcome the mentioned issues of previous works. The FGSLR methods capture the spectral correlation via low-rank factorization, meanwhile utilizing factor group sparsity regularization to further enhance the low-rank property. It is SVD-free and robust to rank selection. Moreover, FGSLR is equivalent to Schatten <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula>-norm approximation (<xref ref-type="theorem" rid="theorem1">Theorem 1 ), and thus FGSLR is tighter than the nuclear norm in terms of rank approximation. To preserve the spatial information of HSI in the denoising process, the total variation regularization is also incorporated into the proposed FGSLR models. Specifically, the proximal alternating minimization is designed to solve the proposed FGSLR models. Experimental results have demonstrated that the proposed FGSLR methods significantly outperform existing low-rank approximation-based HSI denoising methods.
Mirror-image aptamers made from chirally inverted nucleic acids are nuclease-resistant and exceptionally biostable, opening up opportunities for unique applications. However, the directed evolution ...and selection of mirror-image aptamers directly from large randomized L-DNA libraries has, to our knowledge, not been demonstrated previously. Here, we developed a 'mirror-image selection' scheme for the directed evolution and selection of biostable L-DNA aptamers with a mirror-image DNA polymerase. We performed iterative rounds of enrichment and mirror-image polymerase chain reaction (PCR) amplification of L-DNA sequences that bind native human thrombin, in conjunction with denaturing gradient gel electrophoresis (DGGE) to isolate individual aptamers and L-DNA sequencing-by-synthesis to determine their sequences. Based on the selected L-DNA aptamers, we designed biostable thrombin sensors and inhibitors, which remained functional in physiologically relevant nuclease-rich environments, even in the presence of human serum that rapidly degraded D-DNA aptamers. Mirror-image selection of biostable L-DNA aptamers directly from large randomized L-DNA libraries greatly expands the range of biomolecules that can be targeted, broadening their applications as biostable sensors, therapeutics and basic research tools.
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