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2. Improving stability and resilience of building structure with steel-timber composite columns: A case study and simulation analysis

Author

Zhao Wei, Huang Bin, Xiao Meng, Liao Lida, Yang Jianyu, Huang Kan, and Xing Ke

Subjects
Steel-timber composite material, Composite structural column, Lightweight building, Hysteretic performance, Finite element analysis, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Research on green buildings is receiving increasing attention in areas such as mitigating global climate change and promoting sustainability in the built environment. This study is based on the experimental and simulation analysis of embedded composite beam-column elements to investigate their potential in improving the sustainability, structural stability and seismic capacity of lightweight residential buildings. A case study of Tianyuan II villa in Changsha city district, China, is used to demonstrate the proposed methodology and validate the rationality of the design solution. In this case, the conventional hollow steel tube columns were replaced by a novel embedded composite column design derived from an optimized steel-to-wood ratio. This alternative approach aims to improve the load carrying capacity and buckling resistance of the building elements, as well as the overall structural integrity of the building frame, especially under seismic conditions. The study shows that: the overall dimensional stability of the building with this structural column under seismic action is better, the hysteresis curve of the column is full, which reflects a strong seismic wave absorption capacity; the damage is mainly manifested by the fracture of the column foot joints and the tearing caused by the non-uniform contact; and the structural column is able to replace the pure steel columns and to ensure the safety and stability of the building.

Published
2024
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3. GreenEyes: An Air Quality Evaluating Model based on WaveNet

Author

Huang, Kan, Zhang, Kai, and Liu, Ming

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Accompanying rapid industrialization, humans are suffering from serious air pollution problems. The demand for air quality prediction is becoming more and more important to the government's policy-making and people's daily life. In this paper, We propose GreenEyes -- a deep neural network model, which consists of a WaveNet-based backbone block for learning representations of sequences and an LSTM with a Temporal Attention module for capturing the hidden interactions between features of multi-channel inputs. To evaluate the effectiveness of our proposed method, we carry out several experiments including an ablation study on our collected and preprocessed air quality data near HKUST. The experimental results show our model can effectively predict the air quality level of the next timestamp given any segment of the air quality data from the data set. We have also released our standalone dataset at https://github.com/AI-Huang/IAQI_Dataset The model and code for this paper are publicly available at https://github.com/AI-Huang/AirEvaluation

Published
2022

13. Anti-tumor efficacy of HRS-4642 and its potential combination with proteasome inhibition in KRAS G12D-mutant cancer

Author

Zhou, Caicun, Li, Chongyang, Luo, Libo, Li, Xin, Jia, Keyi, He, Ning, Mao, Shiqi, Wang, Wanying, Shao, Chuchu, Liu, Xinyu, Huang, Kan, Yu, Yaxin, Cai, Xinlei, Chen, Yingxue, Dai, Zican, Li, Wei, Yu, Jia, Li, Jiayu, Shen, Feng, Wang, Zaiyong, He, Feng, Sun, Xing, Mao, Rongfu, Shi, Wei, Zhang, Jun, Jiang, Tao, Zhang, Zhe, Li, Fei, and Ren, Shengxiang

Published
2024
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16. Gut microbial co-metabolite 2-methylbutyrylcarnitine exacerbates thrombosis via binding to and activating integrin α2β1

Author

Huang, Kan, Li, Zilun, He, Xi, Dai, Jun, Huang, Bingding, Shi, Yongxia, Fan, Dongxiao, Zhang, Zefeng, Liu, Yunchong, Li, Na, Zhang, Zhongyu, Peng, Jiangyun, Liu, Chenshu, Zeng, Renli, Cen, Zhipeng, Wang, Tengyao, Yang, Wenchao, Cen, Meifeng, Li, Jingyu, Yuan, Shuai, Zhang, Lu, Hu, Dandan, Huang, Shuxiang, Chen, Pin, Lai, Peilong, Lin, Liyan, Wen, Jielu, Zhao, Zhengde, Huang, Xiuyi, Yuan, Lining, Zhou, Lifang, Wu, Haoliang, Huang, Lihua, Feng, Kai, Wang, Jian, Liao, Baolin, Cai, Weiping, Deng, Xilong, Li, Yueping, Li, Jianping, Hu, Zhongwei, Yang, Li, Li, Jiaojiao, Zhuo, Youguang, Zhang, Fuchun, Lin, Lin, Luo, Yifeng, Zhang, Wei, Ni, Qianlin, Hong, Xiqiang, Chang, Guangqi, Zhang, Yang, Guan, Dongxian, Cai, Weikang, Lu, Yutong, Li, Fang, Yan, Li, Ren, Meng, Li, Linghua, and Chen, Sifan

Published
2024
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30. PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection

Author

Zhu, Chunbiao, Cai, Xing, Huang, Kan, Li, Thomas H, and Li, Ge

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Multimedia
Abstract

Fully convolutional neural networks (FCNs) have shown outstanding performance in many computer vision tasks including salient object detection. However, there still remains two issues needed to be addressed in deep learning based saliency detection. One is the lack of tremendous amount of annotated data to train a network. The other is the lack of robustness for extracting salient objects in images containing complex scenes. In this paper, we present a new architecture$ - $PDNet, a robust prior-model guided depth-enhanced network for RGB-D salient object detection. In contrast to existing works, in which RGB-D values of image pixels are fed directly to a network, the proposed architecture is composed of a master network for processing RGB values, and a sub-network making full use of depth cues and incorporate depth-based features into the master network. To overcome the limited size of the labeled RGB-D dataset for training, we employ a large conventional RGB dataset to pre-train the master network, which proves to contribute largely to the final accuracy. Extensive evaluations over five benchmark datasets demonstrate that our proposed method performs favorably against the state-of-the-art approaches., Comment: This paper is under review. Project website: https://github.com/ChunbiaoZhu/PDNet/

Published
2018

38. Robust Saliency Detection via Fusing Foreground and Background Priors

Author

Huang, Kan, Zhu, Chunbiao, and Li, Ge

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Automatic Salient object detection has received tremendous attention from research community and has been an increasingly important tool in many computer vision tasks. This paper proposes a novel bottom-up salient object detection framework which considers both foreground and background cues. First, A series of background and foreground seeds are selected from an image reliably, and then used for calculation of saliency map separately. Next, a combination of foreground and background saliency map is performed. Last, a refinement step based on geodesic distance is utilized to enhance salient regions, thus deriving the final saliency map. Particularly we provide a robust scheme for seeds selection which contributes a lot to accuracy improvement in saliency detection. Extensive experimental evaluations demonstrate the effectiveness of our proposed method against other outstanding methods., Comment: Project website: https://github.com/ChunbiaoZhu/FBP

Published
2017

39. Automatic Salient Object Detection for Panoramic Images Using Region Growing and Fixation Prediction Model

Author

Zhu, Chunbiao, Huang, Kan, and Li, Ge

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Almost all previous works on saliency detection have been dedicated to conventional images, however, with the outbreak of panoramic images due to the rapid development of VR or AR technology, it is becoming more challenging, meanwhile valuable for extracting salient contents in panoramic images. In this paper, we propose a novel bottom-up salient object detection framework for panoramic images. First, we employ a spatial density estimation method to roughly extract object proposal regions, with the help of region growing algorithm. Meanwhile, an eye fixation model is utilized to predict visually attractive parts in the image from the perspective of the human visual search mechanism. Then, the previous results are combined by the maxima normalization to get the coarse saliency map. Finally, a refinement step based on geodesic distance is utilized for post-processing to derive the final saliency map. To fairly evaluate the performance of the proposed approach, we propose a high-quality dataset of panoramic images (SalPan). Extensive evaluations demonstrate the effectiveness of our proposed method on panoramic images and the superiority of the proposed method against other methods., Comment: Previous Project website: https://github.com/ChunbiaoZhu/DCC-2018

Published
2017

40. Unraveling a Novel Formation Pathway of Hydroxymethanesulfonate (HMS) in the Marine Atmosphere.

Author

Zhu, Yucheng, Li, Hao, Xing, Chengzhi, Dong, Xinyi, Liu, Cheng, Qin, Xiaofei, Wang, Guochen, Liu, Ziwei, Ding, Zhijian, Tian, Mengke, Deng, Congrui, and Huang, Kan

Abstract

Hydroxymethanesulfonate (HMS, CH2(OH)SO3−) is an important contributor to organosulfates with the potential to impact the biogeochemical sulfur cycle and global climate. However, its characteristics in the marine atmosphere have rarely been investigated. This study investigated the abundances of HMS over the Yellow and East China Seas based on island and cruise observations, showing weak seasonal variations but significant spatial patterns. Aerosol pH was diagnosed as the limiting factor governing the production rates of HMS via the traditional aqueous processing. However, the disparities between the theoretical formation rates and observed concentrations implied the possibility of a new formation mechanism for HMS. Strong correlations were found between methanesulfonate (MSA) and HMS, with machine learning simulation indicating MSA as the most pivotal contributor. Through the density functional theory calculations, HMS can be generated by gas‐phase organic reactions between the •OOCH2SO3H radical (formed from the isomerization of methylsulfonoxyl radical (CH3SO3•) and oxidation) and NO or CH3OO• radical, leading to the cogeneration of HMS and MSA. The HMS production rate via the gas phase process was 3.7–55 times larger than that of aqueous processing, which extends the understanding of formation mechanisms of atmospheric organosulfur compounds. Plain Language Summary: Hydroxymethylsulfonate (HMS) is an organosulfur compound that can be observed in high concentrations during heavy pollution and may alter aerosol acidity and affect atmospheric new particle formation rates. HMS has been increasingly studied in urban areas, but rarely in the marine atmosphere. Only one known aqueous‐phase mechanism has been considered in field observations and model simulation studies, while potential new mechanisms haven't been further explored. Here, we documented the characteristics of HMS and explored its new formation mechanisms over the Yellow and East China Seas based on island and cruise observations. The results showed that the spatial pattern of HMS in the marine atmosphere was more conspicuous than the seasonal variation. The low pH and liquid water content of marine aerosol limited the HMS production from its precursors via aqueous processing, while HMS can be generated by organic reactions, leading to the cogeneration of HMS and methanesulfonate. The gas‐phase reaction is demonstrated as a plausible pathway for the production of HMS in the marine atmosphere. This study sheds a new perspective on the production mechanism of organosulfur compounds and the marine sulfur biogeochemical cycle. Key Points: The seasonal and spatial patterns of particulate hydroxymethylsulfonate (HMS) over the Yellow and East China Seas were first revealedThe production rates of HMS via aqueous‐phase reactions poorly matched the concentrations, mainly due to the low aerosol pHA cogeneration mechanism of HMS and MSA via the CH3SO3• radical was proposed based on machine learning and theoretical calculations [ABSTRACT FROM AUTHOR]

Published
2024
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41. METTL14 plays an oncogenic role in NSCLC by modulating ferroptosis and the m6A modification of GPX4.

Author

Lou, Yang, Huang, Kan, Xu, Bo, and Chen, Xianguo

Abstract

Context: N6-methyladenosine (m6A) of RNA is involved in the progression of non-small cell lung cancer (NSCLC). Objective: This study investigated the role of METTL14 in NSCLC and the mechanism. Materials and methods: Expression levels were assessed by quantitative real-time PCR and ELISA assays. Cells viability was assessed by cell counting kit-8. M6A methylation was analysed by methylated RNA immunoprecipitation (MeRIP), RIP, luciferase assay, and mRNA stability assay. Results: The results showed that METTL14 was highly expressed in NSCLC tissues and cell lines. Knockdown of METTL14 inhibited the cell viability while induced ferroptosis of NSCLC cells. Mechanistically, METTL14 interacts with GPX4, mediates m6A modification of GPX4, enhances its mRNA stability, and upregulates its expression. In addition, IGF2BP1 recognises the m6A-methylated GPX4 and mediates the elevated mRNA stability. Moreover, GPX4 reversed the effects of METTL14 depletion. Discussion and conclusion: The METTL14/GPX4 axis promotes NSCLC progression by inhibiting cell ferroptosis through the recognition of m6A modification mediated by IGF2BP1. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Imatinib and methazolamide ameliorate COVID-19-induced metabolic complications via elevating ACE2 enzymatic activity and inhibiting viral entry

Author

Li, Zilun, Peng, Meixiu, Chen, Pin, Liu, Chenshu, Hu, Ao, Zhang, Yixin, Peng, Jiangyun, Liu, Jiang, Li, Yihui, Li, Wenxue, Zhu, Wei, Guan, Dongxian, Zhang, Yang, Chen, Hongyin, Li, Jiuzhou, Fan, Dongxiao, Huang, Kan, Lin, Fen, Zhang, Zefeng, Guo, Zeling, Luo, Hengli, He, Xi, Zhu, Yuanyuan, Li, Linghua, Huang, Bingding, Cai, Weikang, Gu, Lei, Lu, Yutong, Deng, Kai, Yan, Li, and Chen, Sifan

Published
2022
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44. Managing Emergent Surgery for Ruptured Abdominal Aortic Aneurysm during the COVID-19 Pandemic

Author

Li, Zilun, Qi, Ming, Zhao, Gang, Li, Litao, Zhang, Hongkun, Liu, Chenshu, Zhang, Jian, Liu, Zhengjun, Huang, Shuichuan, Zhu, Guoxian, Zhang, Yan, Zhou, Zhongxin, Wang, Weici, Yang, Chao, Li, Yiqing, Luo, Yingwei, Liu, Bing, Zhang, Bojian, Wang, Jinsong, Lin, Shaomang, Ai, Wenjia, Luo, Canhua, Miao, Jianhang, Zhang, Mingguang, Liu, Chao, Bai, Peiru, Zhang, Zhi, Liao, Bingye, Yin, Henghui, Huang, Kan, Yao, Chen, Wang, Shenming, and Chang, Guangqi

Published
2022
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48. Geometric Hitting Set for Segments of Few Orientations

Author

Fekete, Sándor P., Huang, Kan, Mitchell, Joseph S. B., Parekh, Ojas, and Phillips, Cynthia A.

Subjects
Computer Science - Computational Geometry, F.2.2
Abstract

We study several natural instances of the geometric hitting set problem for input consisting of sets of line segments (and rays, lines) having a small number of distinct slopes. These problems model path monitoring (e.g., on road networks) using the fewest sensors (the "hitting points"). We give approximation algorithms for cases including (i) lines of 3 slopes in the plane, (ii) vertical lines and horizontal segments, (iii) pairs of horizontal/vertical segments. We give hardness and hardness of approximation results for these problems. We prove that the hitting set problem for vertical lines and horizontal rays is polynomially solvable., Comment: 35 pages, 10 figures; full journal version (to appear in Theory of Computing Systems) of conference paper that appears in WAOA 2015, pp. 145-157

Published
2016

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