Effect of chloride and sulfate ions in simulated boiler water on pitting corrosion behavior of 13Cr steel

The pitting corrosion behavior of 13Cr steel was investigated in simulated boiler waters containing chloride ions (Cl-) and sulfate ions (SO42-) using potentiodynamic and potentiostatic polarization tests in addition to pit morphology analysis. The presence of 100 ppm cl(-) in the water caused pitting corrosion of the steel. Pit initiation was inhibited by the addition of 50 ppm or 100 ppm SO42- into the water containing 100 ppm Cl-. Pit growth was also suppressed by the presence of 50 ppm SO42- in the water with 100 ppm Cl-; however, it was conversely promoted in the presence of 100 ppm SO42-. (C) 2015 Elsevier Ltd. All rights reserved.ArticleCORROSION SCIENCE. 96:171-177 (2015)journal articl

Spin Polarized and Valley Helical Edge Modes in Graphene Nanoribbons

Inspired by recent progress in fabricating precisely zigzag-edged graphene nanoribbons and the observation of edge magnetism, we find that spin polarized edge modes with well-defined valley index can exist in a bulk energy gap opened by a staggered sublattice potential such as that provided by a hexagonal Boron-Nitride substrate. Our result is obtained by both tight-binding model and first principles calculations. These edge modes are helical with respect to the valley degree of freedom, and are robust against scattering, as long as the disorder potential is smooth over atomic scale, resulting from the protection of the large momentum separation of the valleys.Comment: 4 pages, 4 figure

NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search

Deep convolutional neural networks (CNNs) have been widely used in surface defect detection. However, no CNN architecture is suitable for all detection tasks and designing effective task-specific requires considerable effort. The neural architecture search (NAS) technology makes it possible to automatically generate adaptive data-driven networks. Here, we propose a new method called NAS-ASDet to adaptively design network for surface defect detection. First, a refined and industry-appropriate search space that can adaptively adjust the feature distribution is designed, which consists of repeatedly stacked basic novel cells with searchable attention operations. Then, a progressive search strategy with a deep supervision mechanism is used to explore the search space faster and better. This method can design high-performance and lightweight defect detection networks with data scarcity in industrial scenarios. The experimental results on four datasets demonstrate that the proposed method achieves superior performance and a relatively lighter model size compared to other competitive methods, including both manual and NAS-based approaches

STUDY ON GYMNASTICS RING MOVEMENTS USING FORCE MEASURING SYSTEM

The purpose of this paper was to analyze five giant-swing phases performed on the rings using force-measuring system, which was synchronized with EMG and film. The results showed similar patterns in pulling force, shoulder angle, hip angle, hip velocity and ankle velocity when performing the movements of backward swing phase, dropped shoulder, giant-swing, and upward swing phase. The pulling-force changed from smaller than the body weight to greater than the body weight in the process of the backward swing. The first peak of pulling force occurred as shoulder drop phase ends. The second peak of pulling force occurred in the backward swing phase. The pulling force decreased gradually in the process of the upward swing

Correction: Zhou, Z.-B. et al. Receptor-Mediated Vascular Smooth Muscle Migration Induced by LPA Involves p38 Mitogen-Activated Protein Kinase Pathway Activation. Int. J. Mol. Sci. 2009, 10, 3194–3208

Due to personal reasons, I left the research group. In accordance with the regulations of the funding institution, Xiamen Health Administration, China, I hereby declare a withdrawal of my signature, Zhi-Jun Zhang, from this paper. [...

Direct Adversarial Training: A New Approach for Stabilizing The Training Process of GANs

Generative Adversarial Networks (GANs) are the most popular models for image generation by optimizing discriminator and generator jointly and gradually. However, instability in training process is still one of the open problems for all GAN-based algorithms. In order to stabilize training, some regularization and normalization techniques have been proposed to make discriminator meet the Lipschitz continuity constraint. In this paper, a new approach inspired by works on adversarial attack is proposed to stabilize the training process of GANs. It is found that sometimes the images generated by the generator play a role just like adversarial examples for discriminator during the training process, which might be a part of the reason of the unstable training. With this discovery, we propose to introduce a adversarial training method into the training process of GANs to improve its stabilization. We prove that this DAT can limit the Lipschitz constant of the discriminator adaptively. The advanced performance of the proposed method is verified on multiple baseline and SOTA networks, such as DCGAN, WGAN, Spectral Normalization GAN, Self-supervised GAN and Information Maximum GAN

FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations

While the majority of existing pre-trained models from code learn source code features such as code tokens and abstract syntax trees, there are some other works that focus on learning from compiler intermediate representations (IRs). Existing IR-based models typically utilize IR features such as instructions, control and data flow graphs (CDFGs), call graphs, etc. However, these methods confuse variable nodes and instruction nodes in a CDFG and fail to distinguish different types of flows, and the neural networks they use fail to capture long-distance dependencies and have over-smoothing and over-squashing problems. To address these weaknesses, we propose FAIR, a Flow type-Aware pre-trained model for IR that involves employing (1) a novel input representation of IR programs; (2) Graph Transformer to address over-smoothing, over-squashing and long-dependencies problems; and (3) five pre-training tasks that we specifically propose to enable FAIR to learn the semantics of IR tokens, flow type information, and the overall representation of IR. Experimental results show that FAIR can achieve state-of-the-art results on four code-related downstream tasks.Comment: ICSE 2024 First Cycl