Artificial intelligence: A powerful paradigm for scientific research

Xu, Yongjun; Liu, Xin; Cao, Xin; Huang, Changping; Liu, Enke; Qian, S.; Liu, Xingchen; Wu, Yicheng; Dong, Fengzhong; Qiu, Cheng‐Wei; Qiu, Junjun; Hua, Keqin; Su, Wentao; Wu, Jian; Xu, Huiyu; Han, Yong; Fu, Chenguang; Yin, Zhigang; Liu, Miao; Roepman, Ronald; Dietmann, Sabine; Virta, Marko; Kengara, Fredrick Orori; Zhang, Ze; Zhang, Lifu; Zhao, Taolan; Dai, Ji; Yang, Jialiang; Liang, Liuqin; Luo, Ming; Zhao-feng, Liu; Tao, An; Zhang, Bin; He, Xin; Cong, Shan; Liu, Xiaohong; Zhang, Wei; Lewis, James P.; Tiedje, James M.; Qi, Wang; An, Zhulin; Wang, Fei; Zhang, Libo; Huang, Tao; Lü, Chuan; Cai, Zhipeng; Fang, Wang; Zhang, Jiabao

doi:10.1016/j.xinn.2021.100179

Cited by 549 publications

(295 citation statements)

References 158 publications

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“…In short, we must pay attention to the differences in metabolism between nanomaterials and molecules, as well as the resulting different requirements for radiolabeling. Furthermore, the introduction of a higher level of automations and even artificial intelligence in the design and practice of radiolabeling will definitely bring new options for the radiotracing study of NMs ( Zhang et al, 2019 ; Xu et al, 2021 ).…”

Section: In Vivo Stability Of Radiolabelingmentioning

confidence: 99%

Radiolabeling of Nanomaterials: Advantages and Challenges

et al. 2021

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Quantifying the distribution of nanomaterials in complex samples is of great significance to the toxicological research of nanomaterials as well as their clinical applications. Radiotracer technology is a powerful tool for biological and environmental tracing of nanomaterials because it has the advantages of high sensitivity and high reliability, and can be matched with some spatially resolved technologies for non-invasive, real-time detection. However, the radiolabeling operation of nanomaterials is relatively complicated, and fundamental studies on how to optimize the experimental procedures for the best radiolabeling of nanomaterials are still needed. This minireview looks back into the methods of radiolabeling of nanomaterials in previous work, and highlights the superiority of the “last-step” labeling strategy. At the same time, the problems existing in the stability test of radiolabeling and the suggestions for further improvement are also addressed.

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Section: In Vivo Stability Of Radiolabelingmentioning

confidence: 99%

Radiolabeling of Nanomaterials: Advantages and Challenges

et al. 2021

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“…Existing sequence inference models can be also employed in trajectory classification, such as Dynamic Bayesian Network (DBN) [ 18 ], and Hidden Markov Model (HMM) [ 19 ] which incorporate the information from locations and the sequential patterns between adjacent locations [ 10 ]. In recent years, artificial intelligence has been widely used in various fields [ 20 ]. Refs.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification

Sun

Zhang

et al. 2022

Sensors

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Ship type classification is an essential task in maritime navigation domains, contributing to shipping monitoring, analysis, and forecasting. Presently, with the development of ship positioning and monitoring systems, many ship trajectory acquisitions make it possible to classify ships according to their movement pattern. Existing methods of ship classification based on trajectory include classical sequence analysis and deep learning methods. However, the real ship trajectories are unevenly distributed in geographical space, which leads to many problems in inferring the ship movement mode on the original ship trajectory. This paper proposes a hierarchical spatial-temporal embedding method based on enhanced trajectory features for ship type classification. We first preprocess the trajectory and combine the port information to transform the original ship trajectory into the moored records of ships, removing the unevenly distributed points in the trajectory data and enhancing key points’ semantic information. Then, we propose a Hierarchical Spatial-Temporal Embedding Method (Hi-STEM) for ship classification. Hi-STEM maps moored records in the original geographical space into the feature space and can efficiently find the classification plane in the feature space. Experiments are conducted on real-world datasets and compared with several existing methods. The result shows that our approach has high accuracy in ship classification on ship moored records. We make the source code and datasets publicly available.

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“…The color deviation impacts the reliability and utility in underwater applications [4,5]. There is no doubt that the color improvement of underwater images is significant for underwater applications [6][7][8][9], and the scope of color improvement in underwater images has received considerable attention in recent decades [10,11]. To solve the color deviation of the underwater image, lots of research has been conducted, which can be classified into two categories, including color restoration with prior information of light attenuation, and color enhancement without the information of light attenuation.…”

Section: Introduction 1the Background Of Color Improvement In Marine ...mentioning

confidence: 99%

The Color Improvement of Underwater Images Based on Light Source and Detector

Quan

Wei

et al. 2022

Sensors

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As one of the most direct approaches to perceive the world, optical images can provide plenty of useful information for underwater applications. However, underwater images often present color deviation due to the light attenuation in the water, which reduces the efficiency and accuracy in underwater applications. To improve the color reproduction of underwater images, we proposed a method with adjusting the spectral component of the light source and the spectral response of the detector. Then, we built the experimental setup to study the color deviation of underwater images with different lamps and different cameras. The experimental results showed that, a) in terms of light source, the color deviation of an underwater image with warm light LED (Light Emitting Diode) (with the value of Δa*2+Δb*2 being 26.58) was the smallest compared with other lamps, b) in terms of detectors, the color deviation of images with the 3×CMOS RGB camera (a novel underwater camera with three CMOS sensors developed for suppressing the color deviation in our team) (with the value of Δa*2+Δb*2 being 25.25) was the smallest compared with other cameras. The experimental result (i.e., the result of color improvement between different lamps or between different cameras) verified our assumption that the underwater image color could be improved by adjusting the spectral component of the light source and the spectral response of the detector. Differing from the color improvement method with image processing, this color-improvement method was based on hardware, which had advantages, including more image information being retained and less-time being consumed.

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Artificial intelligence: A powerful paradigm for scientific research

Cited by 549 publications

References 158 publications

Radiolabeling of Nanomaterials: Advantages and Challenges

Radiolabeling of Nanomaterials: Advantages and Challenges

A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification

The Color Improvement of Underwater Images Based on Light Source and Detector

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