Effect of C Addition on the Microstructure and Fracture Properties of In Situ Laminated Nb/Nb5Si3 Composites.

//Nbss and α-Nb5Si3 phases were detected. Meanwhile, Nb2C was observed, and the crystal forms of Nb5Si3 changed in the C-doped composites. Furthermore, micron-sized and nano-sized Nb2C particles were found in the Nbss layer. The orientation relationship of Nb2C phase and the surrounding Nbss was [001]Nbss//[010]Nb2C, (200) Nbss//(101) Nb2C. Additionally, with the addition of C, the compressive strength of the composites, at 1400 °C, and the fracture toughness increased from 310 MPa and 11.9 MPa·m1/2 to 330 MPa and 14.2 MPa·m1/2, respectively; the addition of C mainly resulted in solid solution strengthening.

A Multiscale Approach to Deep Blind Image Quality Assessment.

Faithful measurement of perceptual quality is of significant importance to various multimedia applications. By fully utilizing reference images, full-reference image quality assessment (FR-IQA) methods usually achieves better prediction performance. On the other hand, no-reference image quality assessment (NR-IQA), also known as blind image quality assessment (BIQA), which does not consider the reference image, makes it a challenging but important task. Previous NR-IQA methods have focused on spatial measures at the expense of information in the available frequency bands. In this paper, we present a multiscale deep blind image quality assessment method (BIQA, M.D.) with spatial optimal-scale filtering analysis. Motivated by the multi-channel behavior of the human visual system and contrast sensitivity function, we decompose an image into a number of spatial frequency bands by multiscale filtering and extract features for mapping an image to its subjective quality score by applying convolutional neural network. Experimental results show that BIQA, M.D. compares well with existing NR-IQA methods and generalizes well across datasets.

Measuring the rogue wave pattern triggered from Gaussian perturbations by deep learning.

Weak Gaussian perturbations on a plane wave background could trigger lots of rogue waves (RWs), due to modulational instability. Numerical simulations showed that these RWs seemed to have similar unit structure. However, to the best of our knowledge, there are no relative results to prove that these RWs have the similar patterns for different perturbations, partly due to that it is hard to measure the RW pattern automatically. In this work, we address these problems from the perspective of computer vision via using deep neural networks. We propose a rogue wave detection network (RWD-Net) model to automatically and accurately detect RWs in the images, which directly indicates they have the similar computer vision patterns. For this purpose, we herein meanwhile have designed and release the corresponding dataset, termed as rogue wave dataset-10K (RWD-10K), which has 10191 RW images with bounding box annotations for each RW unit. In our detection experiments, we get 99.29% average precision on the test splits of the proposed dataset. Finally, we derive our metric, termed as the density of RW units, to characterize the evolution of Gaussian perturbations and obtain the statistical results on them.

Current opinions on the mechanism, classification, imaging diagnosis and treatment of post-traumatic osteomyelitis.

Post-traumatic osteomyelitis (PTO) is a worldwide problem in the field of orthopaedic trauma. So far, there is no ideal treatment or consensus-based gold standard for its management. This paper reviews the representative literature focusing on PTO, mainly from the following four aspects: (1) the pathophysiological mechanism of PTO and the interaction mechanism between bacteria and the body, including fracture stress, different components of internal fixation devices, immune response, occurrence and development mechanisms of inflammation in PTO, as well as the occurrence and development mechanisms of PTO in skeletal system; (2) clinical classification, mainly the etiological classification, histological classification, anatomical classification and the newly proposed new classifications (a brief analysis of their scope and limitations); (3) imaging diagnosis, including non-invasive examination and invasive examination (this paper discusses their advantages and disadvantages respectively, and briefly compares the sensitivity and effectiveness of the current examinations); and (4) strategies, including antibiotic administration, surgical choices and other treatment programs. Based on the above-mentioned four aspects, we try to put forward some noteworthy sections, in order to make the existing opinions more specific.

Quantitative study of starch swelling capacity during gelatinization with an efficient automatic segmentation methodology.

A novel image segmentation methodology combined with optical microscopy observation was developed for qualifying starch swelling. Starch granules in the micrograph were successfully segmented based on high-precision edges extraction achieved by Canny edge detection together with mathematical morphology operation. Granules were automatically identified by computer vision and characterized by giving quantifiable area of these granules. The evolved swelling process could be generally divided into two phases. During the first phase, starch granules were only swollen up by 2.56 %, which is hard to be identified by conventional naked eye. During the following narrow temperature interval (60-66 ℃), these starch granules were detected to swell up significantly by 9.08 %. Through the granule area variable, swelling capacity was high-throughput characterized, which allows for the whole evaluation to be completed within a couple of minutes. The proposed methodology showed a high accuracy and potential as a novel technique for characterizing gelatinization.

Self-Supervised Time Series Clustering With Model-Based Dynamics.

Time series clustering is usually an essential unsupervised task in cases when category information is not available and has a wide range of applications. However, existing time series clustering methods usually either ignore temporal dynamics of time series or isolate the feature extraction from clustering tasks without considering the interaction between them. In this article, a time series clustering framework named self-supervised time series clustering network (STCN) is proposed to optimize the feature extraction and clustering simultaneously. In the feature extraction module, a recurrent neural network (RNN) conducts a one-step time series prediction that acts as the reconstruction of the input data, capturing the temporal dynamics and maintaining the local structures of the time series. The parameters of the output layer of the RNN are regarded as model-based dynamic features and then fed into a self-supervised clustering module to obtain the predicted labels. To bridge the gap between these two modules, we employ spectral analysis to constrain the similar features to have the same pseudoclass labels and align the predicted labels with pseudolabels as well. STCN is trained by iteratively updating the model parameters and the pseudoclass labels. Experiments conducted on extensive time series data sets show that STCN has state-of-the-art performance, and the visualization analysis also demonstrates the effectiveness of the proposed model.

Biomimetic and osteogenic 3D silk fibroin composite scaffolds with nano MgO and mineralized hydroxyapatite for bone regeneration.

Artificial bioactive materials have received increasing attention worldwide in clinical orthopedics to repair bone defects that are caused by trauma, infections or tumors, especially dedicated to the multifunctional composite effect of materials. In this study, a weakly alkaline, biomimetic and osteogenic, three-dimensional composite scaffold (3DS) with hydroxyapatite (HAp) and nano magnesium oxide (MgO) embedded in fiber (F) of silkworm cocoon and silk fibroin (SF) is evaluated comprehensively for its bone repair potential in vivo and in vitro experiments, particularly focusing on the combined effect between HAp and MgO. Magnesium ions (Mg2+) has long been proven to promote bone tissue regeneration, and HAp is provided with osteoconductive properties. Interestingly, the weak alkaline microenvironment from MgO may also be crucial to promote Sprague-Dawley (SD) rat bone mesenchymal stem cells (BMSCs) proliferation, osteogenic differentiation and alkaline phosphatase (ALP) activities. This SF/F/HAp/nano MgO (SFFHM) 3DS with superior biocompatibility and biodegradability has better mechanical properties, BMSCs proliferation ability, osteogenic activity and differentiation potential compared with the scaffolds adding HAp or MgO alone or neither. Similarly, corresponding meaningful results are also demonstrated in a model of distal lateral femoral defect in SD rat. Therefore, we provide a promising 3D composite scaffold for promoting bone regeneration applications in bone tissue engineering.

Research Progress on Diagnosis and Treatment of Chronic Osteomyelitis.

We review the representatives literatures on chronic osteomyelitis, sum up the new insights in recent years into diagnostic options and treatment regimens, analyze the advantages and disadvantages of various diagnostic approaches and treatment strategies, and propose areas of interest to make current diagnostic and treatment strategies more specific.

Diazoxide ameliorates severity of experimental osteoarthritis by activating autophagy via modulation of the osteoarthritis-related biomarkers.

Accumulating evidence suggests that autophagy plays a protective role in chondrocytes and prevents cartilage degeneration in osteoarthritis (OA). The objective of this study was to investigate the effect of diazoxide on chondrocyte death and cartilage degeneration and to determine whether these effects are correlated to autophagy in experimental OA. In this study, a cellular OA model was established by stimulating SW1353 cells with interleukin 1ß. A rat OA model was generated by transecting the anterior cruciate ligament combined with the resection of the medial menisci, followed by treatment with diazoxide or diazoxide combination with 3-methyladenine. The percentage of viable cells was evaluated using calcein-acetoxymethyl/propidium iodide double staining. The messenger RNA expression levels of collagen type II alpha 1 chain (COL2A1), matrix metalloproteinase 13 (MMP-13), TIMP metallopeptidase inhibitor 1 (TIMP-1), and a disintegrin and metalloproteinase with thrombospondin motifs 5 (ADAMTS5) were determined using quantitative real-time polymerase chain reaction. The cartilage thickness and joint space were evaluated using ultrasound. SW1353 cell degeneration and autophagosomes were observed using transmission electron microscopy. The expression levels of microtubule-associated protein 1 light chain 3 (LC3), beclin-1, P62, COL2A1, and MMP-13 were evaluated using immunofluorescence staining and Western blot analysis. Diazoxide significantly attenuated articular cartilage degeneration and SW1353 cell death in experimental OA. The restoration of autophagy was observed in the diazoxide-treated group. The beneficial effects of diazoxide were markedly blocked by 3-methyladenine. Diazoxide treatment also modulated the expression levels of OA-related biomarkers. These results demonstrated that diazoxide exerted a chondroprotective effect and attenuated cartilage degeneration by restoring autophagy via modulation of OA-related biomarkers in experimental OA. Diazoxide treatment might be a promising therapeutic approach to prevent the development of OA.

Rapid reconstruction of quantitative susceptibility mapping via improved ℓ0 norm approximation.

Quantitative susceptibility mapping (QSM) reconstruction is a well-known ill-posed problem. Various regularization techniques have been proposed for solving this problem. In this paper, a rapid method is proposed that uses ℓ0 norm minimization in a gradient domain. Because ℓ0 minimization is an NP-hard problem, a special alternating optimization strategy is employed to simplify the reconstruction algorithm. The proposed algorithm uses only simple point-wise multiplications and thresholding operations, and significantly speeds up the calculation. Both numerical simulations and in vivo experiments demonstrate that the proposed method can reconstruct susceptibility fast and accurately. Because morphology information weighted methods have achieved considerable success in QSM, we performed a quantitative comparison with some typical weighted methods, such as MEDI (morphology enabled dipole inversion), iLSQR (improved least squares algorithm), and wℓ1 (weighted ℓ1 norm minimization). The reconstructed results show that the proposed method can provide accurate results with a satisfactory speed.

A Probabilistic Method for Image Enhancement With Simultaneous Illumination and Reflectance Estimation.

In this paper, a new probabilistic method for image enhancement is presented based on a simultaneous estimation of illumination and reflectance in the linear domain. We show that the linear domain model can better represent prior information for better estimation of reflectance and illumination than the logarithmic domain. A maximum a posteriori (MAP) formulation is employed with priors of both illumination and reflectance. To estimate illumination and reflectance effectively, an alternating direction method of multipliers is adopted to solve the MAP problem. The experimental results show the satisfactory performance of the proposed method to obtain reflectance and illumination with visually pleasing enhanced results and a promising convergence rate. Compared with other testing methods, the proposed method yields comparable or better results on both subjective and objective assessments.

Image segmentation based on the Poincaré map method.

Active contour models (ACMs) integrated with various kinds of external force fields to pull the contours to the exact boundaries have shown their powerful abilities in object segmentation. However, local minimum problems still exist within these models, particularly the vector field's "equilibrium issues." Different from traditional ACMs, within this paper, the task of object segmentation is achieved in a novel manner by the Poincaré map method in a defined vector field in view of dynamical systems. An interpolated swirling and attracting flow (ISAF) vector field is first generated for the observed image. Then, the states on the limit cycles of the ISAF are located by the convergence of Newton-Raphson sequences on the given Poincaré sections. Meanwhile, the periods of limit cycles are determined. Consequently, the objects' boundaries are represented by integral equations with the corresponding converged states and periods. Experiments and comparisons with some traditional external force field methods are done to exhibit the superiority of the proposed method in cases of complex concave boundary segmentation, multiple-object segmentation, and initialization flexibility. In addition, it is more computationally efficient than traditional ACMs by solving the problem in some lower dimensional subspace without using level-set methods.

Coarse-to-fine boundary location with a SOM-like method.

A coarse-to-fine boundary location with a self-organizing map (SOM)-like method is proposed in this paper. Inspired from the conventional SOM and universal gravitation, given a small quantity of supervision seeds from the desired boundaries, neurons are used to evolve to the desired boundaries in a coarse-to-fine framework. The major components of this framework are the designs of union action and evolving rate. In the course of neuron evolution, the union actions acting on these neurons will offer them the evolving directions. Also controlled by the corresponding referenced gradients, the neurons' evolving rates are adaptively adjusted at different positions. With the union actions and evolving rates, the neurons will evolve with appropriate manners to expand the set of feature points on the desired boundaries. The newly expanded feature points will cause the generation updates for feature points and neurons, and offer new information to guide the new generation of neurons to the boundaries. What is more, the proposed multiround evolution is as well a coarse-to-fine way for boundary location. Experiments and comparisons show that the proposed method performs well in complex long concavities, inhomogeneous and weak boundary location with good initialization flexibility.

Arranging and interpolating sparse unorganized feature points with geodesic circular arc.

A novel method to reconstruct object boundaries with geodesic circular arc is proposed in this paper. Within this framework, an energy of circular arc spline is utilized to simultaneously arrange and interpolate each member in the set of sparse unorganized feature points from the desired boundaries. A general form for a family of parametric circular arc spline is firstly derived and followed by a novel method of arranging these feature points by minimizing an energy term depending on the circular arc spline configuration defined on these feature points. With regard to the fact that the energy function is usually nonconvex and nondifferentiable at its critical points, an improved scheme of particle swarm optimizer is given to find the minimum for the energy in this paper. With this improved scheme, each pair of neighboring feature points along the boundaries of the desired objects are picked out from the set of sparse unorganized feature points, and the corresponding directional chord tangent angles are computed simultaneously to finish interpolation. We show experimentally and comparatively that the proposed method can perform effectively to restrict leakage on weak boundaries and premature convergence on long concave boundaries. Besides, it has good noise robustness and can as well extract multiple and open boundaries.