Data Security Storage Mechanism Based on Blockchain Network

With the rapid development of information technology,the development of blockchain technology has also been deeply impacted.When performing block verification in the blockchain network,if all transactions are verified on the chain,this will cause the accumulation of data on the chain,resulting in data storage problems.At the same time,the security of data is also challenged,which will put enormous pressure on the block,resulting in extremely low communication efficiency of the block.The traditional blockchain system uses theMerkle Tree method to store data.While verifying the integrity and correctness of the data,the amount of proof is large,and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency,which will cause end-to-end communication delays and seriously affect the blockchain system’s stability,efficiency,and security.In order to solve this problem,this paper proposes to replace the Merkle tree with polynomial commitments,which take advantage of the properties of polynomials to reduce the proof size and communication consumption.By realizing the ingenious use of aggregated proof and smart contracts,the verification efficiency of blocks is improved,and the pressure of node communication is reduced.

Fluorescent aptasensor for detection of live foodborne pathogens based on multicolor perovskite-quantum-dot-encoded DNA probes and dual-stirring-bar-assisted signal amplification

In this study,a fluorescent(FL)aptasensor was developed for on-site detection of live Salmonella typhimurium(S.T.)and Vibrio parahaemolyticus(V.P.).Complementary DNA(cDNA)of aptamer(Apt)-functionalized multicolor polyhedral oligomeric silsesquioxane-perovskite quantum dots(cDNA-POSSPQDs)were used as encoded probes and combined with dual-stirring-bar-assisted signal amplification for pathogen quantification.In this system,bar 1 was labeled with the S.T.and V.P.Apts,and then bar 2 was functionalized with cDNA-POSS-PQDs.When S.T.and V.P.were introduced,pathogen-Apt complexes would form and be released into the supernatant from bar 1.Under agitation,the two complexes reached bar 2 and subsequently reacted with cDNA-POSS-PQDs,which were immobilized on MXene.Then,the encoded probes would be detached from bar 2 to generate FL signals in the supernatant.Notably,the pathogens can resume their free state and initiate next cycle.They swim between the two bars,and the FL signals can be gradually enhanced to maximum after several cycles.The FL signals from released encoded probes can be used to detect the analytes.In particular,live pathogens can be distinguished from dead ones by using an assay.The detection limits and linear range for S.T.and V.P.were 30 and 10 CFU/mL and 10^(2) -10^(6) CFU/mL,respectively.Therefore,this assay has broad application potential for simultaneous on-site detection of various live pathogenic bacteria in water.

Failure prognostic of systems with hidden degradation process

Systems with a hidden degradation process are perva- sive in the real world. Degrading critical components will under- mine system performance and pose potential failures in the future. Prognostic aims at predicting potential failures before it evolves into faults. A prognostic procedure based on expectation maxi- mization and unscented Kalman filter is proposed. System state, sensor measurement and hidden degradation process are viewed as data （incomplete or missing） in the expectation maximization method. System state and hidden degradation process are esti- mated by a unscented Kalman filter upon sensor measurements. Component-specific parameters in a degradation process are iden- tified on the estimation of the degradation process. Residual life is measured by the median of estimated residual life distribution. The proposed procedure is verified by simulations on a first-order capacitor-resistance circuit with degrading resistance. Residual life estimation consists conservatively with the trend and is evalu- ated in terms of relative errors. Simulation results are reasonable. The proposed prognostic method expects applications in practice.

Recent advances and applications of single atom catalysts based electrochemical sensors

Single atom catalysts(SACs)have attracted considerable attention due to their unique structures and excellent catalytic performance,especially in the area of catalysis science and energy conversion and storage.In recent years,SACs have emerged as a new type of sensing material for constructing electrochemical sensors(ECSs),presenting excellent sensitivity,selectivity,and stability.Herein,we review the recent advances of SACs in electrochemical sensing and discuss the status quo of current SAC-based ECSs.Specifically,the fundamentals of SAC-based ECSs are outlined,including the involved central metal atoms and various supports of SACs in this field,the detection mechanisms,and improving strategies of SAC-based ECSs.Moreover,the important applications of SAC-based ECSs are listed and classified,covering the detection of reactive oxygen and nitrogen species,environmental pollutants,disease biomarkers,and pharmaceuticals.Last,based on abundant reported cases,the current conundrums of SAC-based ECSs are summarized,and the prediction of their future developing trends is also put forward.

PVT v2:Improved baselines with Pyramid Vision Transformer

Transformers have recently lead to encouraging progress in computer vision.In this work,we present new baselines by improving the original Pyramid Vision Transformer(PVT v1)by adding three designs:(i)a linear complexity attention layer,(ii)an overlapping patch embedding,and(iii)a convolutional feed-forward network.With these modifications,PVT v2 reduces the computational complexity of PVT v1 to linearity and provides significant improvements on fundamental vision tasks such as classification,detection,and segmentation.In particular,PVT v2 achieves comparable or better performance than recent work such as the Swin transformer.We hope this work will facilitate state-ofthe-art transformer research in computer vision.Code is available at https://github.com/whai362/PVT.