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6 results on '"Ruihao Gong"'

1. MgO coated P2-Na0.67Mn0.75Ni0.25O2 layered oxide cathode for Na-Ion batteries

Author

Cornelius Gauckler, Gints Kucinskis, Lukas Fridolin Pfeiffer, Abdelaziz A. Abdellatif, Yushu Tang, Christian Kübel, Fabio Maroni, Ruihao Gong, Margret Wohlfahrt-Mehrens, Peter Axmann, and Mario Marinaro

Subjects
Coating, Layered oxide, Magnesium, Batteries, Sodium, Industrial electrochemistry, TP250-261, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4
Abstract

In this study, we propose an effective strategy to improve the electrochemical performance of a P2-Na0.67Mn0.75Ni0.25O2 (P2-MNO) cathode material for Na-ion batteries based on MgO surface coating. The MgO coating, with a thickness of ∼20–50 nm, is obtained by means of a facile wet-chemistry approach followed by heat treatment carried out at comparatively low temperatures (400–500 °C) in order to avoid possible Mg doping in the bulk of the P2-MNO. Detailed electrochemical investigations demonstrate improved electrochemical performance of the MgO-coated material (M-P2-MNO) in comparison to pristine bare one at both room and elevated (40 °C) temperatures. Operando differential electrochemical mass spectroscopy (DEMS) demonstrate that the MgO coating is effective in suppressing unwanted gas evolution due to side reactions thus stabilizing the cathode/electrolyte interface.

Published
2024
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2. The Antimicrobial Peptide LJ-hep2 from Lateolabrax japonicus Exerting Activities against Multiple Pathogenic Bacteria and Immune Protection In Vivo

Author

Ruihao Gong, Zhe An, Weibin Zhang, Fangyi Chen, and Ke-Jian Wang

Subjects
Lateolabrax japonicus, hepcidin, antimicrobial peptide, antimicrobial activity, multidrug-resistant bacteria, immunoprotective effect, Biology (General), QH301-705.5
Abstract

Hepcidin is widely present in many kinds of fish and is an important innate immune factor. A variety of HAMP2-type hepcidins have strong antimicrobial activity and immunomodulatory functions and are expected to be developed as substitutes for antibiotics. In this study, the antimicrobial activity of Hepc2 from Japanese seabass (Lateolabrax japonicus) (designated as LJ-hep2) was investigated using its recombinant precursor protein (rLJ-hep2) expressed in Pichia pastoris and a chemically synthesized mature peptide (LJ-hep2(66–86)). The results showed that both rLJ-hep2 and synthetic LJ-hep2(66–86) displayed broad antimicrobial spectrum with potent activity against gram-negative and gram-positive bacteria, and fungi. Especially, LJ-hep2(66–86) had stronger antimicrobial activity and exhibited potent activity against several clinically isolated multidrug-resistant bacteria, including Acinetobacter baumannii, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae and Enterococcus faecium. Moreover, LJ-hep2(66–86) exerted rapid bactericidal kinetic (killed tested bacteria within 2 h), induced significant morphological changes and promoted agglutination of E. coli, P. aeruginosa and Aeromonas hydrophila. The activity of LJ-hep2(66–86) against E. coli, P. aeruginosa and A. hydrophila was stable and remained active when heated for 30 min. In addition, LJ-hep2(66–86) exhibited no cytotoxicity to the mammalian cell line HEK293T and fish cell lines (EPC and ZF4). In vivo study showed that LJ-hep2(66–86) could improve the survival rate of marine medaka (Oryzias melastigma) by about 40% under the challenge of A. hydrophila, indicating its immunoprotective function. Taken together, both rLJ-hep2 and LJ-hep2(66–86) have good prospects to be used as potential antimicrobial agents in aquaculture and medicine in the future.

Published
2022
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3. Anatase-Wrapped Rutile Nanorods as an Effective Electron Collector in Hybrid Photoanodes for Visible Light-Driven Oxygen Evolution

Author

Ruihao Gong, Dariusz Mitoraj, Robert Leiter, Manuel Mundszinger, Alexander K. Mengele, Igor Krivtsov, Johannes Biskupek, Ute Kaiser, Radim Beranek, and Sven Rau

Subjects
anatase-wrapped rutile nanorods, electron collector, hybrid, photoanode, visible light, oxygen evolution, Chemistry, QD1-999
Abstract

Arrays of single crystal TiO2 rutile nanorods (RNRs) appear highly promising as electron-collecting substrates in hybrid photoanodes as the RNRs offer direct charge carriers transport pathways, contrary to the conventional electrodes prepared from TiO2 powders that suffer from the numerous charge traps at the grain boundaries. However, the specific surface area of the nanorods is highly limited by their smooth morphology, which might be detrimental in view of utilizing the RNR as a substrate for immobilizing other functional materials. In this study, we developed a novel anatase-wrapped RNR (ARNR) material fabricated by a facile seed layer-free hydrothermal method. The ARNR comprises polycrystalline anatase nanoparticles formed on the surface of RNR, resulting in a large surface area that provides more deposition sites compared to the bare nanorods. Herein, we functionalize ARNR and RNR electrodes with polymeric carbon nitride (CNx) coupled with a CoO(OH)x cocatalyst for dioxygen evolution. The anatase wrapping of the rutile nanorod scaffold is found to be crucial for effective deposition of CNx and for improved photoanode operation in visible light-driven (λ > 420 nm) oxygen evolution, yielding a significant enhancement of photocurrent (by the factor of ∼3.7 at 1.23 V vs. RHE) and faradaic efficiency of oxygen evolution (by the factor of ∼2) as compared to photoanodes without anatase interlayer. This study thus highlights the importance of careful interfacial engineering in constructing photoelectrocatalytic systems for solar energy conversion and paves the way for the use of ARNR-based electron collectors in further hybrid and composite photochemical architectures for solar fuel production.

Published
2021
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4. Distribution-sensitive Information Retention for Accurate Binary Neural Network

Author

Haotong Qin, Xiangguo Zhang, Ruihao Gong, Yifu Ding, Yi Xu, and Xianglong Liu

Subjects
FOS: Computer and information sciences, Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition, Software
Abstract

Model binarization is an effective method of compressing neural networks and accelerating their inference process. However, a significant performance gap still exists between the 1-bit model and the 32-bit one. The empirical study shows that binarization causes a great loss of information in the forward and backward propagation. We present a novel Distribution-sensitive Information Retention Network (DIR-Net) that retains the information in the forward and backward propagation by improving internal propagation and introducing external representations. The DIR-Net mainly relies on three technical contributions: (1) Information Maximized Binarization (IMB): minimizing the information loss and the binarization error of weights/activations simultaneously by weight balance and standardization; (2) Distribution-sensitive Two-stage Estimator (DTE): retaining the information of gradients by distribution-sensitive soft approximation by jointly considering the updating capability and accurate gradient; (3) Representation-align Binarization-aware Distillation (RBD): retaining the representation information by distilling the representations between full-precision and binarized networks. The DIR-Net investigates both forward and backward processes of BNNs from the unified information perspective, thereby providing new insight into the mechanism of network binarization. The three techniques in our DIR-Net are versatile and effective and can be applied in various structures to improve BNNs. Comprehensive experiments on the image classification and objective detection tasks show that our DIR-Net consistently outperforms the state-of-the-art binarization approaches under mainstream and compact architectures, such as ResNet, VGG, EfficientNet, DARTS, and MobileNet. Additionally, we conduct our DIR-Net on real-world resource-limited devices which achieves 11.1x storage saving and 5.4x speedup.

Published
2021

5. Generalizing From Limited Resources in the Open World : Second International Workshop, GLOW 2024, Held in Conjunction with IJCAI 2024, Jeju, South Korea, August 3, 2024, Proceedings

Author

Jinyang Guo, Yuqing Ma, Yifu Ding, Ruihao Gong, Xingyu Zheng, Changyi He, Yantao Lu, Xianglong Liu, Jinyang Guo, Yuqing Ma, Yifu Ding, Ruihao Gong, Xingyu Zheng, Changyi He, Yantao Lu, and Xianglong Liu

Subjects
Artificial intelligence, Computer science, Computers, Application software
Abstract

This book presents the Proceedings from the Second International Workshop GLOW 2024 held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI 2024, in Jeju Island, South Korea, in August 2024. The 11 full papers and 4 short papers included in this book were carefully reviewed and selected from 22 submissions. They were organized in topical sections as follows: efficient methods for low-resource hardware; efficient fintuning with limited data; advancements in multimodal systems; recognition and reasoning in the open world.

Published
2024

6. Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks

Author

Fengwei Yu, Ruihao Gong, Tianxiang Li, Peng Hu, Xianglong Liu, Jiazhen Lin, Junjie Yan, and Shenghu Jiang

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Bridging (networking), Speedup, Artificial neural network, Computer science, business.industry, Computer Vision and Pattern Recognition (cs.CV), Quantization (signal processing), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, 010501 environmental sciences, Electrical Engineering and Systems Science - Image and Video Processing, 01 natural sciences, Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Differentiable function, Artificial intelligence, business, Algorithm, 0105 earth and related environmental sciences
Abstract

Hardware-friendly network quantization (e.g., binary/uniform quantization) can efficiently accelerate the inference and meanwhile reduce memory consumption of the deep neural networks, which is crucial for model deployment on resource-limited devices like mobile phones. However, due to the discreteness of low-bit quantization, existing quantization methods often face the unstable training process and severe performance degradation. To address this problem, in this paper we propose Differentiable Soft Quantization (DSQ) to bridge the gap between the full-precision and low-bit networks. DSQ can automatically evolve during training to gradually approximate the standard quantization. Owing to its differentiable property, DSQ can help pursue the accurate gradients in backward propagation, and reduce the quantization loss in forward process with an appropriate clipping range. Extensive experiments over several popular network structures show that training low-bit neural networks with DSQ can consistently outperform state-of-the-art quantization methods. Besides, our first efficient implementation for deploying 2 to 4-bit DSQ on devices with ARM architecture achieves up to 1.7$\times$ speed up, compared with the open-source 8-bit high-performance inference framework NCNN. [31], IEEE ICCV 2019

Published
2019

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