Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully construct...Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.展开更多
Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fund...Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fundamental trade-off between haze and transparency,coupled with impractical thicknesses(≥1 mm).Inspired by squid’s skin-peeling mechanism,this work develops a peroxyformic acid(HCOOOH)-enabled precision peeling strategy to isolate intact 10-μm-thick bamboo green(BG)frameworks—100×thinner than wood-based counterparts while achieving an unprecedented optical performance(88%haze with 80%transparency).This performance surpasses delignified biomass(transparency<40%at 1 mm)and matches engineered cellulose composites,yet requires no energy-intensive nanofibrillation.The preserved native cellulose I crystalline structure(64.76%crystallinity)and wax-coated uniaxial fibril alignment(Hermans factor:0.23)contribute to high mechanical strength(903 MPa modulus)and broadband light scattering.As a light-management layer in polycrystalline silicon solar cells,the BG framework boosts photoelectric conversion efficiency by 0.41%absolute(18.74%→19.15%),outperforming synthetic anti-reflective coatings.The work establishes a scalable,waste-to-wealth route for optical-grade cellulose materials in next-generation optoelectronics.展开更多
CO_(2)reduction technology can promote the resource utilization of carbon and help alleviate global warming and energy supply pressure.It is an effective way to achieve energy conversion and utilization.Covalent organ...CO_(2)reduction technology can promote the resource utilization of carbon and help alleviate global warming and energy supply pressure.It is an effective way to achieve energy conversion and utilization.Covalent organic frameworks(COFs)are porous crystalline materials formed by connecting organic monomers through covalent bonds.They have the characteristics of functional diversity and rich chemical properties.Their advantages,such as high porosity,a wide range of visible light absorption,and excellent charge separation efficiency,give them good potential in CO_(2)capture,separation,and conversion.Currently,Cu is a key metal in the catalytic CO_(2)reduction reaction(CO_(2)RR)for the preparation of high-value-added chemicals.The preparation of highly stable and large-pore Cu-based COFs using COFs as an ideal sacrificial template for loading Cu can be used to develop high-performance electrocatalysts and photocatalysts.In this review,we discuss the latest advancements in this field,including the development of various Cu-based COFs and their applications as catalysts for CO_(2)RR.Here,we mainly introduce the synthesis strategies,some important characterization information,and the applications of electrocatalytic and photocatalytic CO_(2)conversion using these previously reported Cu-based COFs.展开更多
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
为了提高锂离子电池健康状态(state of health,SOH)估计的精确度,本研究结合卷积神经网络(convolutional neural networks,CNN)强大的局部特征提取能力和Transformer的序列处理能力,提出了基于多项式特征扩展的CNN-Transformer融合模型...为了提高锂离子电池健康状态(state of health,SOH)估计的精确度,本研究结合卷积神经网络(convolutional neural networks,CNN)强大的局部特征提取能力和Transformer的序列处理能力,提出了基于多项式特征扩展的CNN-Transformer融合模型。该方法提取了与电池容量高度相关的增量容量(incremental capacity,IC)曲线峰值、IC曲线对应电压、面积及充电时间作为健康因子,然后将其进行多项式扩展,增加融合模型对输入特征的非线性处理能力。引入主成分分析法(principal component analysis,PCA)对特征空间进行降维,有利于捕获数据有效信息,减少模型训练时间。采用美国国家宇航局(National Aeronautics and Space Administration,NASA)数据集和马里兰大学数据集,通过加入多项式特征前后的CNN-Transformer模型对比、加入多项式特征的CNN-Transformer模型和单一模型算法对比,验证了加入多项式特征的CNN-Transformer融合算法的有效性和精确度,结果表明提出模型的SOH估计精度相较于未加入多项式特征的CNN-Transformer模型,对于B0005、B0006、B0007、B0018数据集分别提高了38.71%、50.28%、4.71%、17.58%。展开更多
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
文摘Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.
基金supported by National Natural Science Foundation of China(32494793).
文摘Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fundamental trade-off between haze and transparency,coupled with impractical thicknesses(≥1 mm).Inspired by squid’s skin-peeling mechanism,this work develops a peroxyformic acid(HCOOOH)-enabled precision peeling strategy to isolate intact 10-μm-thick bamboo green(BG)frameworks—100×thinner than wood-based counterparts while achieving an unprecedented optical performance(88%haze with 80%transparency).This performance surpasses delignified biomass(transparency<40%at 1 mm)and matches engineered cellulose composites,yet requires no energy-intensive nanofibrillation.The preserved native cellulose I crystalline structure(64.76%crystallinity)and wax-coated uniaxial fibril alignment(Hermans factor:0.23)contribute to high mechanical strength(903 MPa modulus)and broadband light scattering.As a light-management layer in polycrystalline silicon solar cells,the BG framework boosts photoelectric conversion efficiency by 0.41%absolute(18.74%→19.15%),outperforming synthetic anti-reflective coatings.The work establishes a scalable,waste-to-wealth route for optical-grade cellulose materials in next-generation optoelectronics.
文摘CO_(2)reduction technology can promote the resource utilization of carbon and help alleviate global warming and energy supply pressure.It is an effective way to achieve energy conversion and utilization.Covalent organic frameworks(COFs)are porous crystalline materials formed by connecting organic monomers through covalent bonds.They have the characteristics of functional diversity and rich chemical properties.Their advantages,such as high porosity,a wide range of visible light absorption,and excellent charge separation efficiency,give them good potential in CO_(2)capture,separation,and conversion.Currently,Cu is a key metal in the catalytic CO_(2)reduction reaction(CO_(2)RR)for the preparation of high-value-added chemicals.The preparation of highly stable and large-pore Cu-based COFs using COFs as an ideal sacrificial template for loading Cu can be used to develop high-performance electrocatalysts and photocatalysts.In this review,we discuss the latest advancements in this field,including the development of various Cu-based COFs and their applications as catalysts for CO_(2)RR.Here,we mainly introduce the synthesis strategies,some important characterization information,and the applications of electrocatalytic and photocatalytic CO_(2)conversion using these previously reported Cu-based COFs.
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
文摘为了提高锂离子电池健康状态(state of health,SOH)估计的精确度,本研究结合卷积神经网络(convolutional neural networks,CNN)强大的局部特征提取能力和Transformer的序列处理能力,提出了基于多项式特征扩展的CNN-Transformer融合模型。该方法提取了与电池容量高度相关的增量容量(incremental capacity,IC)曲线峰值、IC曲线对应电压、面积及充电时间作为健康因子,然后将其进行多项式扩展,增加融合模型对输入特征的非线性处理能力。引入主成分分析法(principal component analysis,PCA)对特征空间进行降维,有利于捕获数据有效信息,减少模型训练时间。采用美国国家宇航局(National Aeronautics and Space Administration,NASA)数据集和马里兰大学数据集,通过加入多项式特征前后的CNN-Transformer模型对比、加入多项式特征的CNN-Transformer模型和单一模型算法对比,验证了加入多项式特征的CNN-Transformer融合算法的有效性和精确度,结果表明提出模型的SOH估计精度相较于未加入多项式特征的CNN-Transformer模型,对于B0005、B0006、B0007、B0018数据集分别提高了38.71%、50.28%、4.71%、17.58%。
