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581 results on '"Zhu, Qiqi"'

1. Multimodal Urban Areas of Interest Generation via Remote Sensing Imagery and Geographical Prior

Author

Shi, Chuanji, Zhang, Yingying, Wang, Jiaotuan, Guo, Xin, and Zhu, Qiqi

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, 68T99, I.4.9
Abstract

Urban area-of-interest (AOI) refers to an integrated urban functional zone with defined polygonal boundaries. The rapid development of urban commerce has led to increasing demands for highly accurate and timely AOI data. However, existing research primarily focuses on coarse-grained functional zones for urban planning or regional economic analysis, and often neglects the expiration of AOI in the real world. They fail to fulfill the precision demands of Mobile Internet Online-to-Offline (O2O) businesses. These businesses require accuracy down to a specific community, school, or hospital. In this paper, we propose a comprehensive end-to-end multimodal deep learning framework designed for simultaneously detecting accurate AOI boundaries and validating the reliability of AOI by leveraging remote sensing imagery coupled with geographical prior, titled AOITR. Unlike conventional AOI generation methods, such as the Road-cut method that segments road networks at various levels, our approach diverges from semantic segmentation algorithms that depend on pixel-level classification. Instead, our AOITR begins by selecting a point-of-interest (POI) of specific category, and uses it to retrieve corresponding remote sensing imagery and geographical prior such as entrance POIs and road nodes. This information helps to build a multimodal detection model based on transformer encoder-decoder architecture to regress the AOI polygon. Additionally, we utilize the dynamic features from human mobility, nearby POIs, and logistics addresses for AOI reliability evaluation via a cascaded network module. The experimental results reveal that our algorithm achieves a significant improvement on Intersection over Union (IoU) metric, surpassing previous methods by a large margin., Comment: 9 pages, 9 figures

Published
2024

2. Construction of Knowledge Graph Based on Design Thinking Cultivation

Author

Zhu, Qiqi, Zhang, Congpin, Li, Qingyang, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gan, Jianhou, editor, Pan, Yi, editor, Zhou, Juxiang, editor, Liu, Dong, editor, Song, Xianhua, editor, and Lu, Zeguang, editor

Published
2024
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15. A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification

Author

Zhu, Qiqi, Deng, Weihuan, Zheng, Zhuo, Zhong, Yanfei, Guan, Qingfeng, Lin, Weihua, Zhang, Liangpei, and Li, Deren

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great success. However, the deep neural network model has a large parameter space and requires a large number of labeled data. Deep learning methods for HSI classification usually follow a patchwise learning framework. Recently, a fast patch-free global learning (FPGA) architecture was proposed for HSI classification according to global spatial context information. However, FPGA has difficulty extracting the most discriminative features when the sample data is imbalanced. In this paper, a spectral-spatial dependent global learning (SSDGL) framework based on global convolutional long short-term memory (GCL) and global joint attention mechanism (GJAM) is proposed for insufficient and imbalanced HSI classification. In SSDGL, the hierarchically balanced (H-B) sampling strategy and the weighted softmax loss are proposed to address the imbalanced sample problem. To effectively distinguish similar spectral characteristics of land cover types, the GCL module is introduced to extract the long short-term dependency of spectral features. To learn the most discriminative feature representations, the GJAM module is proposed to extract attention areas. The experimental results obtained with three public HSI datasets show that the SSDGL has powerful performance in insufficient and imbalanced sample problems and is superior to other state-of-the-art methods. Code can be obtained at: https://github.com/dengweihuan/SSDGL., Comment: 14 pages,14 figures

Published
2021
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44. The prevalent dynamic and genetic characterization of mcr-1encoding multidrug resistant Escherichia colistrains recovered from poultry in Hebei, China

Author

Wang, Qing, Wang, Weiwei, Zhu, Qiqi, Shoaib, Muhammad, Chengye, Wang, Zhu, Zhen, Wei, Xiaojuan, Bai, Yubin, and Zhang, Jiyu

Abstract

•The genetic environment of mcr-1showed its position on the p0111 plasmid in 2 isolates, a rarely reported plasmid type for the mcr-1gene. Moreover, this type of plasmids was transferable in recipient J53, and mcr-1was encoded by three mobile elements ISApl1, Tn3, and IS26, forming a novel backbone Tn3–IS26-mcr-1–pap2-ISApl1 on the p0111 plasmid.•Four O15: H10-serotype Escherichia coli(E. coli) carrying the mcr-1gene have been identified in this study.•The mcr-1gene can transfer horizontally with much stability in this study.•The mcr-1gene from the present study has homology with mcr-1 E. colifrom other sources, showing its clonal dissemination among the different hosts and species, which poses a potential threat to public health.

Published
2024
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48. Butorphanol inhibits androgen biosynthesis and metabolism in rat immature Leydig cells in vitro.

Author

Li, Changchang, Shi, Lei, Su, Ming, Li, Xiaoheng, Zhu, Qiqi, Ge, Ren-Shan, and Li, Huitao

Subjects
LEYDIG cells, BUTORPHANOL, LUTEINIZING hormone receptors, ANDROGENS, BIOSYNTHESIS, ANDROGEN receptors
Abstract

Butorphanol is a synthetic opioid analgesic medication that is primarily used for the management of pain. Butorphanol may have an inhibitory effect on androgen biosynthesis and metabolism in rat immature Leydig cells. The objective of this study was to investigate the influence of butorphanol on androgen secretion by rat Leydig cells isolated from the 35-day-old male rats. Rat Leydig cells were cultured with 0.5–50 μM butorphanol for 3 h in vitro. Butorphanol at 5 and 50 μM significantly inhibited androgen secretion in immature Leydig cells. At 50 μM, butorphanol also blocked the effects of luteinizing hormone (LH) and 8bromo-cAMP-stimulated androgen secretion and 22R-hydroxycholesterol- and pregnenolone-mediated androgen production. Further analysis of the results showed that butorphanol downregulated the expression of genes involved in androgen production, including Lhcgr (LH receptor), Cyp11a1 (cholesterol side-chain cleavage enzyme), Srd5a1 (5α-reductase 1), and Akr1c14 (3α-hydroxysteroid dehydrogenase). Additionally, butorphanol directly inhibited HSD3B1 (3β-hydroxysteroid dehydrogenase 1) and SRD5A1 activity. In conclusion, butorphanol may have side effects of inhibiting androgen biosynthesis and metabolism in Leydig cells. [ABSTRACT FROM AUTHOR]

Published
2024
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