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Vacancy defect MoSeTe embedded in N and F co-doped carbon skeleton for high performance sodium ion batteries and hybrid capacitors

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Journal of Energy Chemistry 2024年第3期90卷 652-664,I0014页

作者： Dehui Yang Wentao Guo Fei Guo Jiaming Zhu Gang Wang Hui Wang Guanghui Yuan Shenghua Ma Beibei Wang State Key Laboratory of Photon-Technology in Western China Energy International Collaborative Center on Photoelectric Technology and Nano Functional MaterialsInstitute of Photonics&Photon-TechnologyNorthwest UniversityXi’an 710127ShannxiChina Key Laboratory of Synthetic and Natural Functional Molecule Chemistry(Ministry of Education) College of Chemistry&Materials ScienceNorthwest UniversityXi’an 710127ShannxiChina school of Chemistry and Chemical engineering Ankang Research Centre of New Nano-materials Science and TechnologyShaanxi University Innovation Research Institute of Advanced Energy Storage Materials and Battery Technology for Future IndustrializationAnkang UniversityAnkang 725000ShannxiChina

Sodium-ion batteries(SIBs) and hybrid capacitors(SIHCs) have garnered significant attention in energy storage due to their inherent advantages,including high energy density,cost-effectiveness,and enhanced ***,developing high-performance anode materials to improve sodium storage performa nce still remains a major ***,a facile one-pot method has been developed to fabricate a hybrid of MoSeTe nanosheets implanted within the N,F co-doped honeycomb carbon skeleton(MoSeTe/N,F@C).Experimental results demonstrate that the incorporation of large-sized Te atoms into MoSeTe nanosheets enlarges the layer spacing and creates abundant anion vacancies,which effectively facilitate the insertion/extraction of Na^(+) and provide numerous ion adsorption sites for rapid surface capacitive ***,the heteroatoms N,F co-doped honeycomb carbon skeleton with a highly conductive network can restrain the volume expansion and boost reaction kinetics within the *** anticipated,the MoSeTe/N,F@C anode exhibits high reversible capacities along with exceptional cycle *** coupled with Na_(3)V_(2)(PO_(4))_(3)@C(NVPF@C) to form SIB full cells,the anode delivers a reversible specific capacity of 126 mA h g^(-1) after 100 cycles at 0.1 A g^(-1).Furthermore,when combined with AC to form SIHC full cells,the anode demonstrates excellent cycling stability with a reversible specific capacity of50 mA h g^(-1) keeping over 3700 cycles at 1.0 A g^(-1).In situ XRD,ex situ TEM characterization,and theoretical calculations(DFT) further confirm the reversibility of sodium storage in MoSeTe/N,F@C anode materials during electrochemical reactions,highlighting their potential for widespread practical *** work provides new insights into the promising utilization of advanced transition metal dichalcogenides as anode materials for Na^(+)-based energy storage devices.

关键词： MoSeTe N F co-doped honeycomb carbon skeleton Sodium-ion batteries Sodium-ion hybrid capacitor

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo school of Computer and Communication engineering University of Science and Technology BeijingBeijing100083China Beijing engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics engineering Aleppo UniversityAleppoSyria Department of Computer science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang institute of Photoelectronics&Zhejiang institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data science and Artificial intelligence Zarqa UniversityZarqa13100Jordan school of Chemical and Environmental engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing

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Machine intelligence Research 2024年第2期21卷 400-410页

作者： Dezheng Wang Yinglong Wang Fan Yang Liyang Xu Yinong Zhang Yiran Chen Ning Liao school of Automation Southeast UniversityNanjing 210096China Software and Artificial intelligence College Chongqing Institute of EngineeringChongqing 400056China Beijing National Research Center for Information science and Technology(BNRist) Department of AutomationTsinghua UniversityBeijing 100084China Liangjiang International College Chongqing University of TechnologyChongqing 401135China Smart City College Beijing Union UniversityBeijing 100101China

In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure *** data-driven soft sensor is an effective solution because it provides a reliable and stable online estimation of such *** paper employs a deep neural network with multiscale feature extraction layers to build soft sensors,which are applied to the benchmarked Tennessee-Eastman process(TEP)and a real wind farm *** comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advantages over other ***,the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural ***,the multiscale feature extraction layers can powerfully extract dataset ***,the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.

关键词： Multi-scale feature extractor deep neural network(DNN) multirate sampled industrial processes prediction

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Knowledge‐based deep learning system for classifying Alzheimer's disease for multi‐task learning

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CAAI Transactions on intelligence Technology 2024年第4期9卷 805-820页

作者： Amol Dattatray Dhaygude Gaurav Kumar Ameta Ihtiram Raza Khan Pavitar Parkash Singh Renato R.Maaliw III Natrayan Lakshmaiya Mohammad Shabaz Muhammad Attique Khan Hany S.Hussein Hammam Alshazly Department of Data science University of WashingtonSeattleWashingtonUSA Department of Computer science&engineering Parul Institute of TechnologyParul UniversityVadodaraGujaratIndia Department of Computer science Jamia HamdardDelhiIndia Department of Management Lovely Professional UniversityPhagwaraIndia 5 College of engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Mechanical engineering Saveetha School of EngineeringSIMATSChennaiTamil NaduIndia Model institute of engineering and Technology JammuJ&KIndia Department of Computer science HITEC UniversityTaxilaPakistan Department of Computer science and Mathematics Lebanese American UniversityBeirutLebanon Electrical engineering Department College of EngineeringKing Khalid UniversityAbhaSaudi Arabia Electrical engineering Department Aswan UniversityAswanEgypt Faculty of Computers and Information South Valley UniversityQenaEgypt

Deep learning has recently become a viable approach for classifying Alzheimer's disease(AD)in medical ***,existing models struggle to efficiently extract features from medical images and may squander additional information resources for illness *** address these issues,a deep three‐dimensional convolutional neural network incorporating multi‐task learning and attention mechanisms is *** upgraded primary C3D network is utilised to create rougher low‐level feature *** introduces a new convolution block that focuses on the structural aspects of the magnetORCID:ic resonance imaging image and another block that extracts attention weights unique to certain pixel positions in the feature map and multiplies them with the feature map ***,several fully connected layers are used to achieve multi‐task learning,generating three outputs,including the primary classification *** other two outputs employ backpropagation during training to improve the primary classification *** findings show that the authors’proposed method outperforms current approaches for classifying AD,achieving enhanced classification accuracy and other in-dicators on the Alzheimer's disease Neuroimaging Initiative *** authors demonstrate promise for future disease classification studies.

关键词： classification deep learning

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Global-local combined features to detect pain intensity from facial expression images with attention mechanism

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Journal of Electronic science and Technology 2024年第3期22卷 80-93页

作者： Jiang Wu Yi Shi Shun Yan Hong-Mei Yan Yangtze Delta Region institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313001China MOE Key Laboratory for Neuroinformation School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu611731China College of engineering University of California at Santa BarbaraSanta Barbara93106USA

The estimation of pain intensity is critical for medical diagnosis and treatment of *** the development of image monitoring technology and artificial intelligence,automatic pain assessment based on facial expression and behavioral analysis shows a potential value in clinical *** paper reports a framework of convolutional neural network with global and local attention mechanism(GLA-CNN)for the effective detection of pain intensity at four-level thresholds using facial expression ***-CNN includes two modules,namely global attention network(GANet)and local attention network(LANet).LANet is responsible for extracting representative local patch features of faces,while GANet extracts whole facial features to compensate for the ignored correlative features between *** the end,the global correlational and local subtle features are fused for the final estimation of pain *** under the UNBC-McMaster Shoulder Pain database demonstrate that GLA-CNN outperforms other state-of-the-art ***,a visualization analysis is conducted to present the feature map of GLA-CNN,intuitively showing that it can extract not only local pain features but also global correlative facial *** study demonstrates that pain assessment based on facial expression is a non-invasive and feasible method,and can be employed as an auxiliary pain assessment tool in clinical practice.

关键词： Attention Convolutional neural network Facial expression Pain intensity

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Evaluating the thermal environment of urban land surfaces in Yakutsk,a city located in a region of continuous permafrost

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Advances in Climate Change Research 2024年第1期15卷 113-123页

作者： Xiang-Long LI Ze ZHANG Jin-Xin LU Anatoli BROUCHKOV Qing-Kai YAN Qi-Hao YU Sheng-Rong ZHANG Andrey MELNIKOV school of Civil engineering and Transportation Institute of Cold Regions Science and EngineeringPermafrost InstituteNortheast Forestry UniversityHarbin150040China China Railway Qinghai-Tibet Group Company Xining 810000China Department of Geocryology Faculty of GeologyLomonosov Moscow State UniversityMoscow 119234Russian Federation State Key Laboratory of Frozen Soil engineering Northwest Institute of Eco Envi ronment and Re sourcesChinese Academy of SciencesLanzhou 730000China Melnikov Permafrost institute Siberian BranchRussian Academy of ScienceYakutsk 677010Russian Federation

Rapid urbanization has led to changes in the urban land surface thermal ***,there are still much unknown about the urban land surface thermal conditions in permafrost *** is a unique geological environment,changes in the urban land surface thermal environment may trigger geological disasters caused by permafrost *** study utilized remote sensing data and geographic de-tectors to identify the dynamic changes in land surface temperature(LST)and land use/land cover(LU/LC)in Yakutsk,as well as the potential factors contributing to LST *** 1992 and 2020,the built-up area in Yakutsk increased by 36%,and the annual average LST in Yakutsk has risen by 6.67℃,accompanied by an expansion of high-temperature *** ongoing greening efforts,rapid urbanization poses a threat to these green *** in the normalized difference built-up index(NDBI)and land use transfer(LDT)were identified as the primary drivers of urban LST *** integrating geographic detector technology and artificial neural network models,we optimized the selection of input factors in the prediction model and used it to explore the future changes in LST in *** average LST in Yakutsk is expected to reach 23.4℃ and 25.1℃ in 2030 and 2040,respectively,with a further increase in high-temperature *** study provides a reference for ecological,hydrological,and geological assessments of cities in permafrost regions.

关键词： Remote sensing Land surface temperature Land use/Land cover Permafrost Yakutsk

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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Computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer science and engineering Shanghai Jiao Tong UniversityShanghai200240China Global institute of future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Advances and challenges in artificial intelligence text generation

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Frontiers of Information Technology & Electronic engineering 2024年第1期25卷 64-83页

作者： Bing LI Peng YANG Yuankang SUN Zhongjian HU Meng YI school of Computer science and engineering Southeast UniversityNanjing 210000China Key Laboratory of Computer network and Information Integration Ministry of EducationSoutheast UniversityNanjing 210000China

Text generation is an essential research area in artificial intelligence(AI)technology and natural language processing and provides key technical support for the rapid development of AI-generated content(AIGC).It is based on technologies such as natural language processing,machine learning,and deep learning,which enable learning language rules through training models to automatically generate text that meets grammatical and semantic *** this paper,we sort and systematically summarize the main research progress in text generation and review recent text generation papers,focusing on presenting a detailed understanding of the technical *** addition,several typical text generation application systems are ***,we address some challenges and future directions in AI text *** conclude that improving the quality,quantity,interactivity,and adaptability of generated text can help fundamentally advance AI text generation development.

关键词： AI text generation Natural language processing Machine learning Deep learning

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RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on intelligence Technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan school of Computer science and Technology Beijing Institute of TechnologyBeijingChina Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina institute of Informatics University of AmsterdamAmsterdamNetherlands school of Communication engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

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