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Background: Gadolinium‐based contrast agents are commonly used in brain magnetic resonance imaging (MRI), however, they cannot be used by patients with allergic reactions or poor renal function. For long‐term follow‐up patients, gadolinium deposition in the body can cause nephrogenic systemic fibrosis and other potential risks. Purpose: Developing a new method of enhanced image synthesis based on the advantages of multisequence MRI has important clinical value for these patients. In this paper, an end‐to‐end synthesis model structure similarity index measure (SSIM)‐based Dual Constrastive Learning with Attention (SDACL) based on contrastive learning is proposed to synthesize contrast‐enhanced T1 (T1ce) using three unenhanced MRI images of T1, T2, and Flair in patients with glioma. Methods: The model uses the attention–dilation generator to enlarge the receptive field by expanding the residual blocks and to strengthen the feature representation and context learning of multisequence MRI. To enhance the detail and texture performance of the imaged tumor area, a comprehensive loss function combining patch‐level contrast loss and structural similarity loss is created, which can effectively suppress noise and ensure the consistency of synthesized images and real images. Results: The normalized root‐mean‐square error (NRMSE), peak signal‐to‐noise ratio (PSNR), and SSIM of the model on the independent test set are 0.307 ±$\pm$ 0.12, 23.337 ±$\pm$ 3.21, and 0.881 ±$\pm$ 0.05, respectively. Conclusions: Results show this method can be used for the multisequence synthesis of T1ce images, which can provide valuable information for clinical diagnosis. [ABSTRACT FROM AUTHOR]

Objective. Medical image registration aims to find the deformation field that can align two images in a spatial position. A medical image registration method based on U-Net architecture has been proposed currently. However, U-Net architecture has few training parameters, which leads to weak learning ability, and it ignores the adverse effects of image noise on the registration accuracy. The article aims at addressing the problem of weak network learning ability and the adverse effects of noisy images on registration. Approach. Here we propose a novel unsupervised 3D brain image registration framework, which introduces the residual unit and singular value decomposition (SVD) denoising layer on the U-Net architecture. Residual unit solves the problem of network degradation, that is, registration accuracy becomes saturated and then degrades rapidly with the increase in network depth. SVD denoising layer uses the estimated model order for SVD-based low-rank image reconstruction. we use Akaike information criterion to estimate the appropriate model order, which is used to remove noise components. We use the exponential linear unit (ELU) as the activation function, which is more robust to noise than other peers. Main results. The proposed method is evaluated on the publicly available brain MRI datasets: Mindboggle101 and LPBA40. Experimental results demonstrate our method outperforms several state-of-the-art methods for the metric of Dice Score. The mean number of folding voxels and registration time are comparable to state-of-the-art methods. Significance. This study shows that Deep Residual-SVD Network can improve registration accuracy. This study also demonstrate that the residual unit can enhance the learning ability of the network, the SVD denoising layer can denoise effectively, and the ELU is more robust to noise. [ABSTRACT FROM AUTHOR]

The identification and protection of critical nodes have a significant influence on the reliability and robustness of the cyber–physical power system (CPPS), which can effectively reduce the risk of cascading failures. Firstly, this paper establishes a one-to-one coupled cascading fault model, which considers different topological characteristics of power and cyber networks. Based on the interdependence between the two networks, the cascading fault propagation mechanism after node failures in CPPS is analyzed. Then, a critical node identification method based on improved adaptive differential evolution (JADE) is proposed to identify the sets of critical nodes that can maximize the impact on the system load of CPPS. Combined with the cascading failure characteristics of CPPS, in the population generation strategy, the test vectors that do not meet the conditions after the crossover operation are filtered and replaced to improve the search efficiency of the algorithm. Finally, in the case study, an interdependent CPPS is constructed with an IEEE 39-bus system and a scale-free network of the same nodes. The simulation results verify the effectiveness of the proposed node importance assessment strategy. In addition, it is also shown that evaluating the critical nodes in power systems is important for enhancing the robustness and ensuring the security of the power grid operation. • An improved JADE method is proposed to identify the sets of critical nodes in CPPS. • By adopting the identification method of JADE, the sets of critical nodes with a greater impact on the system load can be obtained. • The specific solution representation and the population generation technique of power systems are introduced into the improved JADE method to effectively solve the IM problem of CPPS. • The established cascading failure model provides a feasible way to analyze the propagation mechanism after node failure in CPPS. [ABSTRACT FROM AUTHOR]

The identification of critical nodes is important for safe operation and accident prevention in power grids. With the accelerated development of renewable energies, the uncertainty of renewable energy has brought greater challenges to the node importance assessment of the power system. The electrical spreading probability method is proposed in this paper to identify the critical nodes in the power grid containing renewable energy. First, the uncertainty factors of renewable energy are established through probability density functions, and Monte Carlo simulation and stochastic DC optimum power flow are adopted to effectively deal with the impact of uncertain power output from solar and wind energies. Then, considering the system topology and load loss after cascading failures calculated by Monte Carlo simulation, a method is proposed to calculate the probability of nodes being infected. Finally, according to the Susceptible–Infected model, a method depending on the propagation ability of nodes is proposed to identify the critical nodes in the complex power system. The effectiveness of the proposed ESP method is verified through simulation examples of the modified IEEE39 power system and the modified IEEE118 power system. • An electrical spreading probability method is proposed. • By adopting the ESP method, a more accurate assessment result can be obtained. • More effectively eliminate the uncertainty brought by renewable energy integration. [ABSTRACT FROM AUTHOR]

Nickel (Ni) particles were compounded into polyimide (PI) and PI reinforced with carbon fiber (CF). Role of Ni particles on tribological performance of the PI-based materials sliding against a bearing steel were investigated under boundary and mixed lubrication regimes. Remarkably, PI composites filled with Ni exhibit ultra-low friction and wear. Interestingly, friction- and wear-reduction roles of Ni are much more significant than those of CF. The results provide direct evidence that an extremely thin NiO glaze layer grows on the steel counterface, above which a uniform C-based tribofilm is formed. It is assumed that the NiO layer can catalyze dehydrogenation of oil molecules and promote tribofilm formation. The present work proposes a design concept of high-performance tribofilm for improving boundary lubrication of motion components. • Ni particles were compounded into PI and CF-reinforcing PI. • Ni greatly improve tribological performance of PI composites under harsh lubrication conditions. • Friction- and wear-reduction roles of Ni are much more significant than those of CF. • A thin NiO glaze layer grows on steel counterface, which can promote tribofilm formation. [ABSTRACT FROM AUTHOR]