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From waste to wealth:Coal tar residue derived carbon materials as low-cost anodes for potassium-ion batteries

International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 464-475页

作者： Zhonghua Lu Jun Shen Xin Zhang Lingcong Chao Liang Chen Ding Zhang Tao Wei Shoudong Xu College of Chemical Engineering and Technology Taiyuan University of TechnologyTaiyuan 030024China College of Chemistry Taiyuan University of TechnologyTaiyuan 030024China School of Chemical Engineering and Pharmacy Wuhan Institute of TechnologyWuhan 430205China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiang 212003China

Carbon materials are widely recognized as highly promising electrode materials for various energy storage system *** tar residues(CTR),as a type of carbon-rich solid waste with high value-added utilization,are crucially important for the development of a more sustainable *** this study,we employed a straightforward direct carbonization method within the temperature range of 700-1000℃to convert the worthless solid waste CTR into economically valuable carbon materials as anodes for potassium-ion batteries(PIBs).The effect of carbonization temperature on the microstructure and the potassium ions storage properties of CTR-derived carbons(CTRCs)were systematically explored by structural and morphological characterization,alongside electrochemical performances *** on the co-regulation between the turbine layers,crystal structure,pore structure,functional groups,and electrical conductivity of CTR-derived carbon carbonized at 900℃(CTRC-900H),the electrode material with high reversible capacity of 265.6m Ah·g^(-1)at 50 m A·g^(-1),a desirable cycling stability with 93.8%capacity retention even after 100 cycles,and the remarkable rate performance for PIBs were ***,cyclic voltammetry(CV)at different scan rates and galvanostatic intermittent titration technique(GITT)have been employed to explore the potassium ions storage mechanism and electrochemical kinetics of *** indicate that the electrode behavior is predominantly governed by surface-induced capacitive processes,particularly under high current densities,with the potassium storage mechanism characterized by an“adsorption-weak intercalation”*** work highlights the potential of CTR-based carbon as a promising electrode material category suitable for high-performance PIBs electrodes,while also provides valuable insights into the new avenues for the high value-added utilization of CTR.

关键词： coal tar residue carbon materials anode potassium-ion batteries high value-added

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Chromium phytoextraction and physiological responses of the hyperaccumulator Leersia hexandra Swartz to plant growth-promoting rhizobacterium inoculation

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Frontiers of Environmental science & engineering 2023年第1期17卷 99-110页

作者： Xuehong Zhang Yuanyuan Zhang Dan Zhu Zhiyi Lin Na Sun Chang Su Hua Lin Junjian Zheng College of Life and Environmental Science Guilin University of Electronic TechnologyGuilin 541004China College of Environmental Science and Engineering Guilin University of TechnologyGuilin 541004China College of Mathematics and Science Wuhan Institute of TechnologyWuhan 430205China

Phytoextraction is a promising option for purifying hexavalent chromium(Cr(Ⅵ))-laden wastewater,but the long remediation period incurred by poor growth rate of Cr hyperaccumulators remains a primary hindrance to its large-scale *** this study,we performed a hydroponic experiment to evaluate the feasibility of promoting the growth and phytoextraction efficiency of Cr hyperaccumulator Leersia hexandra Swartz(***)by inoculating plant growth-promoting rhizobacteria(PGPR)Bacillus cereus(***).In batch tests,the Cr(Ⅵ)removal rates of *** and *** co-culture were greater than the sum of their respective *** was likely due to the microbial reduction of Cr(Ⅵ)to Cr(Ⅲ),which is amiable to plant ***,the PGPR factors of ***,including indoleacetic acid(IAA)production,1-aminocyclopropane-1-carboxylic acid deamination(ACCd)activity,phosphate solubilization capacity,and siderophore production,were *** PGPR factors helped explain the biomass augmentation,root elongation and enhanced Cr enrichment of the inoculated *** in pot *** the increased Cr uptake,no aggravated oxidative damage to the cell membrane was observed in the inoculated *** was attributed to its capacity to confront the increased intracellular Cr stress by upregulating both the activities of antioxidative enzymes and expression of metal-binding proteins/***,*** could always conserve the majority of Cr in the residual and oxalic integrated forms with low mobility and phytotoxicity,irrespective of the *** *** results highlight the constructed Cr hyperaccumulatorrhizobacteria consortia as an effective candidate for decontaminating Cr(Ⅵ)-laden wastewater.

关键词： Hexavalent chromium Hyperaccumulator Rhizobacteria Leersia hexandra Swartz Bacillus cereus Consortia

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NeurDB: an AI-powered autonomous data system

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science China(Information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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3D medical image segmentation using the serial-parallel convolutional neural network and transformer based on crosswindow self-attention

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CAAI Transactions on Intelligence technology 2025年第2期10卷 337-348页

作者： Bin Yu Quan Zhou Li Yuan Huageng Liang Pavel Shcherbakov Xuming Zhang Department of Biomedical Engineering College of Life Science and TechnologyHuazhong University of Science and TechnologyWuhanChina Department of Ultrasound Imaging Wuhan Children's Hospital(Wuhan Maternal and Child Healthcare Hospital)Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina Department of Urology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina Institute for Control Science Russian Academy of SciencesMoscowRussia

Convolutional neural network(CNN)with the encoder-decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global *** transformer can extract the global information well but adapting it to small medical datasets is challenging and its computational complexity can be *** this work,a serial and parallel network is proposed for the accurate 3D medical image segmentation by combining CNN and transformer and promoting feature interactions across various semantic *** core components of the proposed method include the cross window self-attention based transformer(CWST)and multi-scale local enhanced(MLE)*** CWST module enhances the global context understanding by partitioning 3D images into non-overlapping windows and calculating sparse global attention between *** MLE module selectively fuses features by computing the voxel attention between different branch features,and uses convolution to strengthen the dense local *** experiments on the prostate,atrium,and pancreas MR/CT image datasets consistently demonstrate the advantage of the proposed method over six popular segmentation models in both qualitative evaluation and quantitative indexes such as dice similarity coefficient,Intersection over Union,95%Hausdorff distance and average symmetric surface distance.

关键词： convolution neural network cross window self‐attention medical image segmentation transformer

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Automatic lung cancer detection using hybrid particle snake swarm optimization with optimized mask RCNN

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Multimedia Tools and Applications 2024年第31期83卷 76807-76831页

作者： Sudha, R. Maheswari, K. M. Uma Department of Computer Science and Engineering School of computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India Department of Computing Technologies School of Computing College of engineering and technology SRM Institute of Science and Technology Kattankulathur India

As a result of its aggressive nature and late identification at advanced stages, lung cancer is one of the leading causes of cancer-related deaths. Lung cancer early diagnosis is a serious and difficult challenge that is crucial to a person's survival. The first diagnosis of the malignant nodules is typically made using chest radiography (X-rays) and computed tomography (CT) scans;however, the potential presence of benign nodules results in incorrect conclusions. The early phases of both benign and malignant nodules exhibit striking similarities. In this paper, a novel deep learning-based model is proposed for the precise diagnosis of malignant nodules. The proposed approach consists of two stages namely, pre-processing and lung nodule detection. Initially, the Lung CT scan images are collected from the dataset. Then, to remove the noise present in the input image, we apply an adaptive median filter. Then, to enhance the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied. After pre-processing, the image is given to the optimized mask RCNN classifier to detect the malignant and benign nodules. To enhance the performance of the Mask RCNN classifier, the hyper-parameters are optimally selected using hybrid particle snake swarm optimization (PS2OA). The proposed PS2OA is a hybridization of particle swarm optimization (PSO) and snake swarm optimization (SSO). The performance of the proposed approach is analyzed based on different metrics and effectiveness compared with state-of-the-art works. The proposed approach attained the maximum accuracy of 97.67%. This work aimed at assisting radiologists to detect and diagnose small-size pulmonary nodules more accurately. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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DSSNet: a transformer-based network for dense scene text detection and recognition in complex environments 5

DSSNet: a transformer-based network for dense scene text det...

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5th International Conference on Signal Processing and computer science, SPCS 2024

作者： Li, Jing Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

This paper presents a novel approach for dense scene text detection called DSSNet (Dense Script Spotter Network). The network leverages ResNet and FPN for feature extraction, employing multi-scale feature fusion and Transformer-based feature processing to enhance text recognition across varying sizes. The method generates text instance shapes using Bézier central curves and performs text recognition by integrating positional query information. Experimental results on the DSTD1500 and ICDAR2015 datasets demonstrate that DSSNet outperforms existing methods in terms of text localization accuracy, recognition accuracy, and annotation flexibility. © 2025 SPIE.

关键词： Feature extraction

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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science China(Information sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU School of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Technology Wuhan University of Science and Technology School of Future Technology South China University of Technology Shenzhen Research Institute of Hunan University School of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 1-13页

作者： S, Balasubramani D, John Aravindhar Renjith, P.N. Ramesh, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment. © 2023

关键词： K-means clustering

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