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
Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based...Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.展开更多
Accelerating the development of new quality productive forces(NQPF),with innovation at its core,has become essential for firm growth in the new era.Drawing on financial data from China's A-share listed companies s...Accelerating the development of new quality productive forces(NQPF),with innovation at its core,has become essential for firm growth in the new era.Drawing on financial data from China's A-share listed companies spanning the period 2010–2023,this study empirically investigates the impact of entrepreneurial spirit on firm-level NQPF.The results indicate that entrepreneurial spirit significantly promotes firm-level NQPF.Mechanism analysis indicates that entrepreneurial effort—underpinned by technological capital accumulation,effective incentive and constraint mechanisms,and a competitive market environment—plays a mediating role in this relationship.Further heterogeneity analysis reveals that,amid China's economic transition,the positive effects of entrepreneurial spirit are more pronounced in non-state-owned enterprises,high-tech firms,and newly established firms.Accordingly,systematic efforts should be pursued across the technological,organizational,and environmental(TOE)dimensions to optimize the cultivation of entrepreneurial spirit.In particular,greater emphasis should be placed on productive entrepreneurial spirit and the constructive role of entrepreneurial effort,so as to fully leverage their contribution to the advancement of firm-level NQPF.展开更多
The recovery of precious metals(PMs)from secondary resources is critical for addressing global supply-chain vulnerabilities and sustainable resource utilization.This review systematically examines the transformative p...The recovery of precious metals(PMs)from secondary resources is critical for addressing global supply-chain vulnerabilities and sustainable resource utilization.This review systematically examines the transformative potential of metal-organic frameworks(MOFs)as next-generation adsorbents for PM recovery,focusing on their synthesis,functionalization,and multiscale adsorption mechanisms.We critically analyze conventional pyrometallurgical and hydrometallurgical methods and highlight their limitations in terms of selectivity,energy consumption,and secondary pollution.In contrast,MOFs offer tunable porosity,abundant active sites,and tunable surface chemistry,enabling efficient PM capture via synergistic physical and chemical adsorption.Advanced modification techniques,including direct synthesis and post-synthetic modification,are reviewed to propose strategies for enhancing the adsorption kinetics and selectivity for Au,Ag,Pt,and Pd.Key structure-property relationships are established through multiscale characterization and thermodynamic models,revealing the critical roles of hierarchical porosity,soft donor atoms,and framework stability.Industrial challenges,such as aqueous stability and scalability,are addressed via Zr-O bond strengthening,hydrophobic functionalization,and support immobilization.This study consolidates the experimental and theoretical advances in MOF-based PM recovery and provides a roadmap for translating laboratory innovations into practical applications within the circular-economy framework.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
Three-dimensional supramolecular organic frameworks with precisely tunable pore sizes are highly demanded for a wide range of applications,e.g.,encapsulating enzymes to enhance their stability,activity,and reusability...Three-dimensional supramolecular organic frameworks with precisely tunable pore sizes are highly demanded for a wide range of applications,e.g.,encapsulating enzymes to enhance their stability,activity,and reusability.However,precise control and tune the pore size of such frameworks still remains a significant challenge to date.In this study,we constructed supramolecular polymer frameworks using rigid tetrahedral star polyisocyanides with tunable length and sufficiently narrow distribution as building block.First,a series of tetrahedral four-arm star polyisocyanides with controlled chain lengths and narrow molecular weight distributions was prepared via the Pd(Ⅱ)-catalyzed living isocyanide polymerization.Then 2-ureido-4[1H]-pyrimidinone(Upy) unit was installed onto each chain-end of polyisocyanide arms via post-polymerization functionalization.Leveraging the supramolecular hydrogen bonding interactions between the terminal Upy units,well-ordered supramolecular polymer frameworks were readily obtained.Notably,the pore size was dependent on the chain length of the polyisocyanide arms.Precisely control the chain length of polyisocyanide arms,supramolecular polymer frameworks with pore sizes ranging from 5.06 nm to 9.72 nm were achieved.These frameworks,with tunable and large pore apertures,demonstrated exceptional capabilities in encapsulating enzymes of different sizes,such as lipase(TL),horseradish peroxidase(HRP),and glucose oxidase(GOx).The encapsulated enzymes exhibited significantly enhanced catalytic activity and durability.Moreover,the frameworks' tunable and large pore apertures facilitated the co-encapsulation of multiple enzymes,enabling efficient dual-enzyme cascade reactions.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
Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contempo...Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contemporary enterprises typically operate 200+interconnected systems,with research indicating that 52% of organizations manage three or more enterprise content management systems,creating information silos that reduce operational efficiency by up to 35%.While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision,their systematic application to business information systems remains largely unexplored.This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System(HABIS)framework that applies multi-level attention mechanisms to enterprise environments.We provide a comprehensive mathematical formulation of the framework,analyze its computational complexity,and present a proof-of-concept implementation with simulation-based validation that demonstrates a 42% reduction in crosssystem query latency compared to legacy ERP modules and 70% improvement in prediction accuracy over baseline methods.The theoretical framework introduces four hierarchical attention levels:system-level attention for dynamic weighting of business systems,process-level attention for business process prioritization,data-level attention for critical information selection,and temporal attention for time-sensitive pattern recognition.Our complexity analysis demonstrates that the framework achieves O(n log n)computational complexity for attention computation,making it scalable to large enterprise environments including retail supply chains with 200+system-scale deployments.The proof-of-concept implementation validates the theoretical framework’s feasibility withMSE loss of 0.439 and response times of 0.000120 s per query,demonstrating its potential for addressing key challenges in business information systems.This work establishes a foundation for future empirical research and practical implementation of attention-driven enterprise systems.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the Hebei Province Science and Technology Plan Project(19221909D)rincess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R308),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.
基金Liaoning Provincial Social Science Fund Key Disciplines Development Project,Research on the New Supply Function of Entrepreneurs Based on Innovation Ecosystems Driven by Data(Grant No.L22ZD061)。
文摘Accelerating the development of new quality productive forces(NQPF),with innovation at its core,has become essential for firm growth in the new era.Drawing on financial data from China's A-share listed companies spanning the period 2010–2023,this study empirically investigates the impact of entrepreneurial spirit on firm-level NQPF.The results indicate that entrepreneurial spirit significantly promotes firm-level NQPF.Mechanism analysis indicates that entrepreneurial effort—underpinned by technological capital accumulation,effective incentive and constraint mechanisms,and a competitive market environment—plays a mediating role in this relationship.Further heterogeneity analysis reveals that,amid China's economic transition,the positive effects of entrepreneurial spirit are more pronounced in non-state-owned enterprises,high-tech firms,and newly established firms.Accordingly,systematic efforts should be pursued across the technological,organizational,and environmental(TOE)dimensions to optimize the cultivation of entrepreneurial spirit.In particular,greater emphasis should be placed on productive entrepreneurial spirit and the constructive role of entrepreneurial effort,so as to fully leverage their contribution to the advancement of firm-level NQPF.
基金supported by the National Natural Science Foundation of China(No.52304329)the Yunnan Fundamental Research Projects(No.202201BE070001-003),Guo Lin would like to acknowledge Xing Dian talent support program of Yunnan Province.
文摘The recovery of precious metals(PMs)from secondary resources is critical for addressing global supply-chain vulnerabilities and sustainable resource utilization.This review systematically examines the transformative potential of metal-organic frameworks(MOFs)as next-generation adsorbents for PM recovery,focusing on their synthesis,functionalization,and multiscale adsorption mechanisms.We critically analyze conventional pyrometallurgical and hydrometallurgical methods and highlight their limitations in terms of selectivity,energy consumption,and secondary pollution.In contrast,MOFs offer tunable porosity,abundant active sites,and tunable surface chemistry,enabling efficient PM capture via synergistic physical and chemical adsorption.Advanced modification techniques,including direct synthesis and post-synthetic modification,are reviewed to propose strategies for enhancing the adsorption kinetics and selectivity for Au,Ag,Pt,and Pd.Key structure-property relationships are established through multiscale characterization and thermodynamic models,revealing the critical roles of hierarchical porosity,soft donor atoms,and framework stability.Industrial challenges,such as aqueous stability and scalability,are addressed via Zr-O bond strengthening,hydrophobic functionalization,and support immobilization.This study consolidates the experimental and theoretical advances in MOF-based PM recovery and provides a roadmap for translating laboratory innovations into practical applications within the circular-economy framework.
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.
基金The National Natural Science Foundation of China (NSFC,Nos.92256201,52273006,22071041,92356302,and 21971052)Natural Science Foundation of Jilin Province (No.20240101181JC) are gratefully appreciated for financial the supportssupported by the User Experiment Assist System of Shanghai Synchrotron Radiation Facility (SSRF)。
文摘Three-dimensional supramolecular organic frameworks with precisely tunable pore sizes are highly demanded for a wide range of applications,e.g.,encapsulating enzymes to enhance their stability,activity,and reusability.However,precise control and tune the pore size of such frameworks still remains a significant challenge to date.In this study,we constructed supramolecular polymer frameworks using rigid tetrahedral star polyisocyanides with tunable length and sufficiently narrow distribution as building block.First,a series of tetrahedral four-arm star polyisocyanides with controlled chain lengths and narrow molecular weight distributions was prepared via the Pd(Ⅱ)-catalyzed living isocyanide polymerization.Then 2-ureido-4[1H]-pyrimidinone(Upy) unit was installed onto each chain-end of polyisocyanide arms via post-polymerization functionalization.Leveraging the supramolecular hydrogen bonding interactions between the terminal Upy units,well-ordered supramolecular polymer frameworks were readily obtained.Notably,the pore size was dependent on the chain length of the polyisocyanide arms.Precisely control the chain length of polyisocyanide arms,supramolecular polymer frameworks with pore sizes ranging from 5.06 nm to 9.72 nm were achieved.These frameworks,with tunable and large pore apertures,demonstrated exceptional capabilities in encapsulating enzymes of different sizes,such as lipase(TL),horseradish peroxidase(HRP),and glucose oxidase(GOx).The encapsulated enzymes exhibited significantly enhanced catalytic activity and durability.Moreover,the frameworks' tunable and large pore apertures facilitated the co-encapsulation of multiple enzymes,enabling efficient dual-enzyme cascade reactions.
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.
文摘Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contemporary enterprises typically operate 200+interconnected systems,with research indicating that 52% of organizations manage three or more enterprise content management systems,creating information silos that reduce operational efficiency by up to 35%.While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision,their systematic application to business information systems remains largely unexplored.This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System(HABIS)framework that applies multi-level attention mechanisms to enterprise environments.We provide a comprehensive mathematical formulation of the framework,analyze its computational complexity,and present a proof-of-concept implementation with simulation-based validation that demonstrates a 42% reduction in crosssystem query latency compared to legacy ERP modules and 70% improvement in prediction accuracy over baseline methods.The theoretical framework introduces four hierarchical attention levels:system-level attention for dynamic weighting of business systems,process-level attention for business process prioritization,data-level attention for critical information selection,and temporal attention for time-sensitive pattern recognition.Our complexity analysis demonstrates that the framework achieves O(n log n)computational complexity for attention computation,making it scalable to large enterprise environments including retail supply chains with 200+system-scale deployments.The proof-of-concept implementation validates the theoretical framework’s feasibility withMSE loss of 0.439 and response times of 0.000120 s per query,demonstrating its potential for addressing key challenges in business information systems.This work establishes a foundation for future empirical research and practical implementation of attention-driven enterprise systems.
