Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data r...
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Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.
The goal of infrared and visible image fusion(IVIF)is to integrate the unique advantages of both modalities to achieve a more comprehensive understanding of a scene. However, existing methods struggle to effectively h...
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The goal of infrared and visible image fusion(IVIF)is to integrate the unique advantages of both modalities to achieve a more comprehensive understanding of a scene. However, existing methods struggle to effectively handle modal disparities,resulting in visual degradation of the details and prominent targets of the fused images. To address these challenges, we introduce Prompt Fusion, a prompt-based approach that harmoniously combines multi-modality images under the guidance of semantic prompts. Firstly, to better characterize the features of different modalities, a contourlet autoencoder is designed to separate and extract the high-/low-frequency components of different modalities, thereby improving the extraction of fine details and textures. We also introduce a prompt learning mechanism using positive and negative prompts, leveraging Vision-Language Models to improve the fusion model's understanding and identification of targets in multi-modality images, leading to improved performance in downstream tasks. Furthermore, we employ bi-level asymptotic convergence optimization. This approach simplifies the intricate non-singleton non-convex bi-level problem into a series of convergent and differentiable single optimization problems that can be effectively resolved through gradient *** approach advances the state-of-the-art, delivering superior fusion quality and boosting the performance of related downstream tasks. Project page: https://***/hey-it-s-me/PromptFusion.
In this study,the efects of diferent heat treatment process parameters on the microstructure and mechanical properties of Al-12Si-5Cu-1.1Mg-2.3Ni-0.3La alloy were *** showed that eutectic Si underwent three stages dur...
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In this study,the efects of diferent heat treatment process parameters on the microstructure and mechanical properties of Al-12Si-5Cu-1.1Mg-2.3Ni-0.3La alloy were *** showed that eutectic Si underwent three stages during solution treatment:difusing,spheroidization and *** the solution temperature and time increased,the size of eutectic Si showed a trend of frst decreasing and then *** with the heat treatment time,the heat treatment temperature had a more signifcant efect on the mechanical *** coarsening of microstructure was the main reason for the deterioration of mechanical *** Al_(3)Ti and Al_(3)CuNiLa in the microstructure after aging can signifcantly improve the mechanical properties of the *** Al_(11)La_(3) with secondary precipitation occurred in the La-rich *** addition of La inhibited the growth of coherent/semi-coherentθandβphases,which was very benefcial for the improvement of high-temperature *** the optimal heat treatment process parameters of 500℃×4 h+190℃×4 h,the ultimate tensile strength(UTS)of the alloy reached 366.65 *** high-temperature strength and elongation of the alloy reached 101.98 MPa and 13.77%at 350℃,respectively.
The integration of machine learning and electrocatalysis presents nota ble advancements in designing and predicting the performance of chiral materials for hydrogen evolution reactions（HER）.This study utilizes theor...
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The integration of machine learning and electrocatalysis presents nota ble advancements in designing and predicting the performance of chiral materials for hydrogen evolution reactions（HER）.This study utilizes theoretical calculations and machine learning techniques to assess the HER performance of both chiral and achiral M-N-SWCNTs（M=In,Bi,and Sb） single-atom catalysts（SACs）.The stability preferences of metal atoms are dependent on chirality when interacting with chiral *** HER activity of the right-handed In-N-SWCNT is 5.71 times greater than its achiral counterpart,whereas the left-handed In-N-SWCNT exhibits a 5.12-fold *** calculated hydrogen adsorption free energy for the right-handed In-N-SWCNT reaches as low as-0.02 *** enhancement is attributed to the symmetry breaking in spin density distribution,transitioning from C2Vin achiral SACs to C2in chiral SACs,which facilitates active site transfer and enhances local spin ***-handed M-N-SWCNTs exhibit superior α-electron separation and transport efficiency relative to left-handed variants,owing to the chiral induced spin selectivity（CISS） effect,with spin-up α-electron density reaching 3.43 × 10-3e/Bohr3at active *** learning provides deeper insights,revealing that the interplay of weak spatial electronic effects and appropriate curvature-chirality effects significantly enhances HER performance.A weaker spatial electronic effect correlates with higher HER activity,larger exchange current density,and higher turnover *** curvature-chirality effect undersco res the influence of intrinsic structures on HER *** findings offer critical insights into the role of chirality in electrocatalysis and propose innovative approaches for optimizing HER through chirality.
This study assesses the watershed vulnerability of Thailand's Nan River Basin (NRB), encompassing both Upper and Lower NRB, under future climate change, land use changes, and water use variations. Using top-perfor...
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For the diagnostics and health management of lithium-ion batteries, numerous models have been developed to understand their degradation characteristics. These models typically fall into two categories:data-driven mode...
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For the diagnostics and health management of lithium-ion batteries, numerous models have been developed to understand their degradation characteristics. These models typically fall into two categories:data-driven models and physical models, each offering unique advantages but also facing ***-informed neural networks(PINNs) provide a robust framework to integrate data-driven models with physical principles, ensuring consistency with underlying physics while enabling generalization across diverse operational conditions. This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV) curves and estimate key ageing parameters at both the cell and electrode *** parameters include available capacity, electrode capacities, and lithium inventory capacity. The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs) and is validated using a public dataset. The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests, with errors in reconstructed OCV curves remaining within 15 mV. This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level, advancing the potential for precise and efficient battery health management.
Graphene encapsulation has been shown to be an effective technique for improving the corrosion resistance of non-noble metal catalysts for the acidic water *** key challenge lies in enhancing the electrocatalytic acti...
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Graphene encapsulation has been shown to be an effective technique for improving the corrosion resistance of non-noble metal catalysts for the acidic water *** key challenge lies in enhancing the electrocatalytic activity of graphene-encapsulated metals while maintaining their durability in acidic ***,an electron-transfer-tuning strategy is investigated at the graphene/NiMo interface,aiming to improve the hydrogen evolution reaction（HER） performance of graphene-encapsulated NiMo *** doping of Ti,a low electronegativity element,into NiMo substrate was confirmed to increase electron transfer from the metal core toward the *** electron-rich state on graphene facilitates the adsorption of positively charged protons on graphene,thereby enabling a Pt/C-comparable performance in 0.5 M H2SO4,with only a 3.8% degradation in performance over a 120-h continuous *** proton exchange membrane（PEM） water electrolyzer assembled by the N-doped grapheneencapsulated Ti-doped NiMo exhibits a smaller cell voltage to achieve a current density of 2.0 A cm-2,in comparison to the Pt/C based *** study proposes a novel electron-transfer-tuning strategy to improve the HER activity of graphene-encapsulated non-noble metal catalysts without sacrificing durability in acidic electrolytes.
The investigation of microplastics(MPs)from the basin level is essential to understanding the fate and transport of MPs and for future modeling *** study explored the MPs in the Upper Ponnaiyar Basin,situated in Tamil...
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The investigation of microplastics(MPs)from the basin level is essential to understanding the fate and transport of MPs and for future modeling *** study explored the MPs in the Upper Ponnaiyar Basin,situated in Tamil Nadu,India,and examined their characteristics and sources in the terrestrial *** extraction of samples was performed using wet peroxide oxidation and subsequent density *** physical and chemical characteristics of extracted samples were analyzed using a stereomicroscope and Raman *** total concentration of MPs was found to be 1941.75 particles/kg,varying from 6.75 to 274.5 particles/*** regions contributed significantly higher concentrations of MPs compared to agricultural *** population showed a positive correlation to MPs concentration in agricultural ***,in rural and urban areas,the correlation was negative and non-existent,*** observation indicated that the improperly disposed wastes in the urban and rural areas were disintegrated due to the heat and photo-induced fragmentation,highlighting the need to develop waste disposal systems in these *** results suggested an evident disparity in the distribution of MPs between the cultivated(24.75±10.45 particles/kg)and uncultivated(18±12.26 particles/kg)agricultural fields,which can be attributed to fertiliser,manure and *** findings of this study can serve as a reference for future studies.
This article tackles the boundary event-based bipartite consensus tracking control problem for the flexible manipulator multi-agent network over a signed diagraph. Each follower agent is the flexible manipulator with ...
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This article tackles the boundary event-based bipartite consensus tracking control problem for the flexible manipulator multi-agent network over a signed diagraph. Each follower agent is the flexible manipulator with unknown disturbances,modeling uncertainties, input saturations and backlashes, and asymmetric output constraints. To reduce the continuous updating of control inputs, a new dynamic event-triggering mechanism is used. Under multiple constraints, achieving the asymptotic convergence point by point in space of the manipulator's vibration state is a control challenge. To solve this issue, we propose a new asymptotic convergence lemma. In control design, radial basis neural networks are employed to estimate nonlinear uncertain terms and the barrier Lyapunov function is used to accomplish the output constraints. Based on the Lyapunov direct method, a novel distributed boundary event-based control algorithm is designed to guarantee that the closed-loop network can reach the asymptotical bipartite consensus tracking and vibration suppression. Moreover, Zeno behaviors can be excluded for each agent. Finally, some numerical results are presented to demonstrate the validity and superiority of the designed control algorithm.
Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar...
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Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.
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