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Micron-Engineered Phosphorus-Rich Biochar Mitigated Metalloid Contamination, Enhanced Bacterial Biomass, and Improved Rice (Oryza Sativa L.) Quality in Antimony Mining Regions

SSRN

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SSRN 2024年

作者： Chen, Hanbo Li, Jianhong Li, Meng Li, Jiayi Sarmah, Ajit K. Zhang, Xiaokai Gao, Yurong Fang, Zheng Yang, Xing Liu, Yuxue Chen, Chongjun Shan, Shengdao Wang, Hailong Key Laboratory of Recycling and Eco-treatment of Waste Biomass of Zhejiang Province School of Environment and Natural Resources Zhejiang University of Science & Technology Hangzhou310023 China Danzhou Soil Environment of Rubber Plantation Hainan Observation and Research Station Rubber Research Institute Chinese Academy of Tropical Agricultural Sciences Hainan Danzhou571737 China Agronomy College Shenyang Agricultural University Shenyang110866 China School of Environmental and Chemical Engineering Foshan University Foshan528000 China Civil and Environmental Engineering Department The Faculty of Engineering The University of Auckland Private Bag 92019 Auckland1142 New Zealand Institute of Environmental Processes and Pollution Control School of Environment and Ecology Jiangnan University Wuxi214122 China School of Environmental Science and Engineering Guangzhou University Guangzhou510006 China Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province School of Ecology and Environment Hainan University Renmin Road Haikou570228 China Institute of Environment Resource Soil and Fertilizer Zhejiang Academy of Agricultural Sciences Hangzhou310021 China School of Environmental Science and Engineering Suzhou University of Science and Technology Suzhou215009 China Guangdong Provincial Key Laboratory of Integrated Agro-environmental Pollution Control and Management Institute of Eco-environmental and Soil Sciences Guangdong Academy of Sciences Guangzhou510650 China

In this study, three ball-milled phosphorus-rich biochars (BMPBs, 1-3) were fabricated based on biochar-to-ball ratios of 1:60, 1:80, and 1:100, respectively. Subsequently, their efficacy in remediating soil contaminated with arsenic (As) and antimony (Sb) was assessed. The ball milling process endowed biochar with enhanced specific surface area (66.4-98.1 m2 g-1) and intensity of functional groups (e.g., C=O and C–O–C). The BMPB addition decreased the soil-available As concentration by 21.4%-25.5%, while only the BMPB-3 treatment caused a significant reduction (14.4%) in the soil-available Sb concentration. Furthermore, BMPB-3 treatment showed the highest increase of 67.4% in the phospholipid fatty acids (PLFA) concentration of Gram-negative bacteria compared to the control, highlighting a positive modulation of microbial community in soils. The application of biochar, particularly BMPB-3, was more effective in promoting the development of iron plaques that enhanced the sequestration of As/Sb in rice roots, ultimately leading to 54.6% and 61.3% reductions in As and Sb uptake by rice grains, respectively. Plant physiological responses indicated that BMPB treatment could mitigate oxidative stress in rice roots by reducing the contents of malondialdehyde and proline due to the activation of catalase and superoxide dismutase activities. Partial least squares path modeling (PLS-PM) analysis indicated that soil properties and the formation of iron plaques after biochar application were pivotal for the bioavailability, translocation, accumulation, and detoxification of As and Sb within soil-rice systems. BMPB-3 is considered as an optimal amendment for remediating As-Sb co-contaminated soil and thereby enhancing rice yield and quality. © 2024, The Authors. All rights reserved.

关键词： Detoxification

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Enrichment, Borrowing, And Mining: A data-Driven Approach To Colonoscopic Lesion Classification

Enrichment, Borrowing, And Mining: A Data-Driven Approach To...

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IEEE International Symposium on Biomedical Imaging

作者： Shuangyi Tan Yuhao Du ZhenHua Wu Dejun Fan Xutao Lin Zhen Li Xiang Wan Guanbin Li The Chinese University of Hong Kong(Shenzhen) Shenzhen Research Institute of Big Data School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Department of Gastrointestinal Endoscopy Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases Biomedical Innovation Center The Sixth Affiliated Hospital Sun Yat-Sen University

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The automatic detection and diagnosis of colorectal lesions during colonoscopy are vital for the prevention and treatment of colorectal cancer. To date, numerous ROI-based neural network models have been proposed to classify colonoscopic lesions. However, they commonly encounter two main issues: 1) Their performance is limited by the dataset size and the diversity of lesion types included; 2) They tend to directly extract deep features from the original Region of Interests (ROIs) of the lesion, neglecting features of the lesion’s edge background as well as detailed texture and color information. To address these issues, we first propose a publicly available colonoscopic lesion classification dataset that includes all major categories of intestinal lesions. Secondly, we innovatively utilize additional colonoscopy video detection data with consistency between frames to enhance the lesion classification model by leveraging an online-target network. Simultaneously, to better mine the characteristics of lesions in data, we propose an ROI broaden strategy and a deep-fusion module, enabling the model to learn lesion-related information such as lesion edges, morphology, texture, and color. Experiments show that our method outperforms existing supervised and semi-supervised state-of-the-art methods, demonstrating the effectiveness of our approach. The GitHub link for our work is https://***/Zoe-TAN/EBM-polyp-cls.

关键词： Image color analysis Image edge detection Prevention and mitigation Colonoscopy Morphology Feature extraction data models

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Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

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Computer Systems Science & engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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ML/MAP Estimation of Frequency Offset and Linear Phase Noise in Coherent Optical Communications

ML/MAP Estimation of Frequency Offset and Linear Phase Noise...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Xinwei Du Qian Wang Pooi-Yuen Kam Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou China School of Science and Engineering Chinese University of Hong Kong Shenzhen Shenzhen China

We propose a joint maximum likelihood (ML)/ maximum a posteriori probability (MAP) frequency offset (FO) and linear phase noise (LPN) estimation scheme for coherent optical communications. By designing the pilot symbols as unmodulated signal with known amplitude, the joint estimation can be simplified to the estimation of frequency and phase of a complex single sinusoid. We then utilize an ML Mth power approach to iteratively estimate the phase on those unknown transmitted symbols. The Cramér-Rao lower bounds are derived and the optimum estimation performance of the proposed algorithms are verified through simulations.

关键词： Phase noise Maximum likelihood estimation Maximum likelihood detection Symbols Frequency estimation Optical fiber communication Optical noise

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CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point Cloud

arXiv

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arXiv 2022年

作者： Ma, Ruiqi Chen, Chi Yang, Bisheng Li, Deren Wang, Haiping Cong, Yangzi Hu, Zongtian State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Engineering Research Center of Space-Time Data Capturing and Smart Application The Ministry of Education of P.R.C. China

At present, the anchor-based or anchor-free models that use LiDAR point clouds for 3D object detection use the center assigner strategy to infer the 3D bounding boxes. However, in a real world scene, the LiDAR can only acquire a limited object surface point clouds, but the center point of the object does not exist. Obtaining the object by aggregating the incomplete surface point clouds will bring a loss of accuracy in direction and dimension estimation. To address this problem, we propose a corner-guided anchor-free single-stage 3D object detection model (CG-SSD1).Firstly, 3D sparse convolution backbone network composed of residual layers and sub-manifold sparse convolutional layers are used to construct bird's eye view (BEV) features for further deeper feature mining by a lite U-shaped network;Secondly, a novel corner-guided auxiliary module (CGAM) is proposed to incorporate corner supervision signals into the neural network. CGAM is explicitly designed and trained to detect partially visible and invisible corners to obtains a more accurate object feature representation, especially for small or partial occluded objects;Finally, the deep features from both the backbone networks and CGAM module are concatenated and fed into the head module to predict the classification and 3D bounding boxes of the objects in the scene. The experiments demonstrate CG-SSD achieves the state-of-art performance on the ONCE benchmark for supervised 3D object detection using single frame point cloud data, with 62.77%mAP. Additionally, the experiments on ONCE and Waymo Open dataset show that CGAM can be extended to most anchor-based models which use the BEV feature to detect objects, as a plug-in and bring +1.17%∼+14.27%AP *** Codes 00-02 Copyright © 2022, The Authors. All rights reserved.

关键词： Object recognition

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Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS

arXiv

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arXiv 2023年

作者： Qiao, Yuanyuan Niu, Yong Han, Zhu Mao, Shiwen He, Ruisi Wang, Ning Zhong, Zhangdui Ai, Bo The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The School of Information Engineering Zhengzhou University Zhengzhou450001 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Due to the development of communication technology and the rise of user network demand, a reasonable resource allocation for wireless networks is the key to guaranteeing regular operation and improving system performance. Various frequency bands exist in the natural network environment, and heterogeneous cellular network (HCN) has become a hot topic for current research. Meanwhile, Reconfigurable Intelligent Surface (RIS) has become a key technology for developing next-generation wireless networks. By modifying the phase of the incident signal arriving at the RIS surface, RIS can improve the signal quality at the receiver and reduce co-channel interference. In this paper, we develop a RIS-assisted HCN model for a multi-base station (BS) multi-frequency network, which includes 4G, 5G, millimeter wave (mmwave), and terahertz networks, and considers the case of multiple network coverage users, which is more in line with the realistic network characteristics and the concept of 6G networks. We propose the optimization objective of maximizing the system sum rate, which is decomposed into two subproblems, i.e., the user resource allocation and the phase shift optimization problem of RIS components. Due to the NP-hard and coupling relationship, we use the block coordinate descent (BCD) method to alternately optimize the local solutions of the coalition game and the local discrete phase search algorithm to obtain the global solution. In contrast, most previous studies have used the coalition game algorithm to solve the resource allocation problem alone. Simulation results show that the algorithm performs better than the rest of the algorithms, effectively improves the system sum rate, and achieves performance close to the optimal solution of the traversal algorithm with low complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Resource allocation

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Atrachinenins D–S, Novel Meroterpenoids with Geranyl Hydroquinone Moiety from Atractylodes Chinensis by the LC/MS-Based Molecular Decoy and Targeted Isolation

SSRN

引用

SSRN 2023年

作者： Chen, Fei-Long Liu, Dong-Li Ren, Wen-Jing Xiong, Hao-Ming Bai, Li-Ping Zhang, Wei Hon, Chitin Jiang, Zhi-Hong Zhu, Guo-Yuan Key Laboratory of Quality Research in Chinese Medicines Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease Macau Institute for Applied Research in Medicine and Health Macau University of Science and Technology China Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Faculty of Innovation Engineering Macau University of Science and Technology Taipa China

To mine fascinating molecules from the rhizomes of Atractylodes chinensis, the known molecular formula of atrachinenin A was used as a bait to search LC-HRMS data in different subfractions. Sixteen new meroterpenoids, atrachinenins D–S (1–16) including three unprecedented carbon skeletons (1–5) and eleven new oxygen-bridged hybrids (6–16) were obtained by the targeted isolation. Their structures and absolute configurations were elucidated by the spectroscopic data and electronic circular dichroism (ECD) calculations. All of the isolated compounds were evaluated for their inhibitory activity of NO production and compounds 1, 4, and 8 showed moderate anti-inflammatory activity. The proposed biosynthetic pathways of 1–5 were also discussed. © 2023, The Authors. All rights reserved.

关键词： Phenols

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A Broadband Linear to Circular Polarization Converter for 5G Millimeter Wave Communication 13

A Broadband Linear to Circular Polarization Converter for 5G...

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13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Ying, Wei-Ning Xu, Tao Lu, Yu He, Guo-Qiang Yang, Xue-Xia School Of Communication And Information Engineering Shanghai200444 China Joint International Research Laboratory Of Specialty Fiber Optics And Advanced Communication Key Laboratory Of Specialty Fiber Optics And Optical Access Networks Shanghai200444 China Shanghai Institute For Advanced Communication And Data Science Shanghai University Shanghai200444 China

ISBN: (纸本)9781665434379

This paper presents a design and simulation of a subwavelenth polarization converter unit operating in 5G millimeter wave frequency. The converter consists of two metal metasurface patterns and a dielectric layer sandwiched by the two metasurfaces. Simulation shows that the converter owns more than 3 GHz bandwidth with axial ratio of circular polarization lower than 3 dB, insertion loss less than 1.5 dB, and VSWR lower than 2, which can adapt to the millimeter wave frequency band of 5G communication. © 2021 IEEE.

关键词： Circular polarization

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A Diabetic Retinopathy Classification Method Based on Novel Attention Mechanism 17th

A Diabetic Retinopathy Classification Method Based on Novel ...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Zou, Jinfan Zhang, Xiaolong Lin, Xiaoli School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783030845216

The convolutional neural network (CNN) has been effectively used to do feature extraction in medical image analysis including fundus images for diabetic retinopathy. However, some important detail features are easily ignored, which causes low prediction accuracy. To solve this problem, a CNN feature extraction method based on a novel pixel-level attention mechanism is proposed to achieve an efficient and accurate identification of diabetic retinopathy. Firstly, data normalization and data augmentation are used to improve the quality of datasets. Then, according to the structure characteristics of the two backbone networks Inception- ResnetV2 and EfficientNet-B5, the corresponding attention modules are introduced respectively. Finally, attention networks are trained as feature extractors of fundus images, and complementary deep attention feature descriptors are formed through feature fusion, which can effectively improve the accuracy of the classification model. The experimental results on EyePACS, Messidor and OIA datasets show that the proposed method outperforms the previous ones. © 2021, Springer Nature Switzerland AG.

关键词： Image classification

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