A Derivative Matrix-Based Covert Communication Method in Blockchain

The data in the blockchain cannot be tampered with and the users are anonymous,which enables the blockchain to be a natural carrier for covert communication.However,the existing methods of covert communication in blockchain suffer from the predefined channel structure,the capacity of a single transaction is not high,and the fixed transaction behaviors will lower the concealment of the communication channel.Therefore,this paper proposes a derivation matrix-based covert communication method in blockchain.It uses dual-key to derive two types of blockchain addresses and then constructs an address matrix by dividing addresses into multiple layers to make full use of the redundancy of addresses.Subsequently,to solve the problem of the lack of concealment caused by the fixed transaction behaviors,divide the rectangular matrix into square blocks with overlapping regions and then encrypt different blocks sequentially to make the transaction behaviors of the channel addresses match better with those of the real addresses.Further,the linear congruence algorithm is used to generate random sequence,which provides a random order for blocks encryption,and thus enhances the security of the encryption algorithm.Experimental results show that this method can effectively reduce the abnormal transaction behaviors of addresses while ensuring the channel transmission efficiency.

Association of point in range withβ-cell function and insulin sensitivity of type 2 diabetes mellitus in cold areas

Background and Objective:Self-monitoring of blood glucose(SMBG)is crucial for achieving a glycemic target and upholding blood glucose stability,both of which are the primary purpose of anti-diabetic treatments.However,the association between time in range(TIR),as assessed by SMBG,andβ-cell insulin secretion as well as insulin sensitivity remains unexplored.Therefore,this study aims to investigate the connections between TIR,derived from SMBG,and indices representingβ-cell functionality and insulin sensitivity.The primary objective of this study was to elucidate the relationship between short-term glycemic control(measured as points in range[PIR])and bothβ-cell function and insulin sensitivity.Methods:This cross-sectional study enrolled 472 hospitalized patients with type 2 diabetes mellitus(T2DM).To assessβ-cell secretion capacity,we employed the insulin secretion-sensitivity index-2(ISSI-2)and(ΔC-peptide_(0-120)/Δglucose_(0-120))×Matsuda index,while insulin sensitivity was evaluated using the Matsuda index and HOMA-IR.Since SMBG offers glucose data at specific point-in-time,we substituted TIR with PIR.According to clinical guidelines,values falling within the range of 3.9-10 mmol were considered"in range,"and the corresponding percentage was calculated as PIR.Results:We observed significant associations between higher PIR quartiles and increased ISSI-2,(ΔC-peptide_(0-120)/Δglucose_(0-120))×Matsuda index,Matsuda index(increased)and HOMA-IR(decreased)(all P<0.001).PIR exhibited positive correlations with log ISSI-2(r=0.361,P<0.001),log(ΔC-peptide_(0-120)/Δglucose_(0-120))×Matsuda index(r=0.482,P<0.001),and log Matsuda index(r=0.178,P<0.001)and negative correlations with log HOMA-IR(r=-0.288,P<0.001).Furthermore,PIR emerged as an independent risk factor for log ISSI-2,log(ΔC-peptide_(0-120)/Δglucose_(0-120))×Matsuda index,log Matsuda index,and log HOMA-IR.Conclusion:PIR can serve as a valuable tool for assessingβ-cell function and insulin sensitivity.

Prediction of the Slope Solute Loss Based on BP Neural Network

The existence of soil macropores is a common phenomenon.Due to the existence of soil macropores,the amount of solute loss carried by water is deeply modified,which affects watershed hydrologic response.In this study,a new improved BP(Back Propagation)neural network method,using Levenberg–Marquand training algorithm,was used to analyze the solute loss on slopes taking into account the soil macropores.The rainfall intensity,duration,the slope,the characteristic scale of macropores and the adsorption coefficient of ions,are used as the variables of network input layer.The network middle layer is used as hidden layer,the number of hidden nodes is five,and a tangent transfer function is used as its neurons transfer function.The cumulative solute loss on the slope is used as the variable of network output layer.A linear transfer function is used as its neurons transfer function.Artificial rainfall simulation experiments are conducted in indoor experimental tanks in order to verify this model.The error analysis and the performance comparison between the proposed method and traditional gradient descent method are done.The results show that the convergence rate and the prediction accuracy of the proposed method are obviously higher than that of traditional gradient descent method.In addition,using the experimental data,the influence of soil macropores on slope solute loss has been further confirmed before the simulation.

Modelling of the Slope Solute Loss Based on Fuzzy Neural Network Model

In regards to soil macropores,the solute loss carried by overland flow is a very complex process.In this study,a fuzzy neural network(FNN)model was used to analyze the solute loss on slopes,taking into account the soil macropores.An artificial rainfall simulation experiment was conducted in indoor experimental tanks,and the verification of the model was based on the results.The characteristic scale of the macropores,the rainfall intensity and duration,the slope and the adsorption coefficient of ions,were chosen as the input variables to the Sugeno FNN model.The cumulative solute loss quantity on the slope was adopted as the output variable of the Sugeno FNN model.There were three membership functions,and the type of membership function was gbellmf(generalized bell membership function).The hybrid learning algorithm,which combines the back propagation algorithm with a least square method,was applied to train and optimize the network parameters,and the optimal network parameters were determined.The simulation results showed that the simulated values were consistent with the measured values.

Studies on Factors Affecting Survival Rate of Micro-grafting in Vitro in Persimmon

Factors affecting micro-graft in vitro were studied in persimmon lines of Jiro,Nishimarawase,Zenjiomaru,Okitsu-20,Xinqiu,Maekawa Jiro and Youhou. The results indicated that higher survival rate was obtained when using the plantlets sub-cultured for 30 d with vigorous adventitious shoots. The best moisturepreserving material was the tampon ＋ parafilm. The optimal medium was（ 1/2 N） MS ＋ BA 3. 0 mg/L ＋ IAA 0. 1 mg/L ＋ sugar 30. 0 g/L ＋ agar 6. 0 g/L,and the graft survival rate was up to 40%.

A method for predicting the probability of formation of complex hydraulic fracture networks in shale reservoirs: development and application

Shales can form a complex fracture network during hydraulic fracturing, which greatly increases the stimulated reservoir volume (SRV) and thus significantly increases oil or gas production. It is therefore important to accurately predict the probability of formation of the hydraulic fracture network for shale gas exploration and exploitation. Conventional discriminant criteria are presented as the relationship curves of stress difference vs. intersection angle. However, these methods are inadequate for application in the field. In this study, an effective and quantitative prediction method relating to the probability of complex fracture network formation is proposed. First, a discriminant criterion of fracture network was derived. Secondly, Monte Carlo simulation was applied to calculate the probability of the formation of the complex fracture network. Then, the method was validated by applying it to individual wells of two active shale gas blocks in the Sichuan Basin, China. Results show that the probabilities of fracture network are 0.98 for well JY1 and 0.26 for well W204, which is consistent with the micro-seismic hydraulic fracturing monitoring and actual gas production. Finally, the method was further extended to apply for the regional scale of the Sichuan Basin, where the general probabilities of fracture network formation are 0.32–1 and 0.74–1 for Weiyuan and Jiaoshiba blocks, respectively. The Jiaoshiba block has, therefore, an overall higher probability for formation of fracture network than the Weiyuan block. The proposed method has the potential in further application to evaluation and prediction of hydraulic fracturing operations in shale reservoirs.

An empirical method for joint inversion of wave and wind parameters based on SAR and wave spectrometer data

Synthetic aperture radar(SAR)and wave spectrometers,crucial in microwave remote sensing,play an essential role in monitoring sea surface wind and wave conditions.However,they face inherent limitations in observing sea surface phenomena.SAR systems,for instance,are hindered by an azimuth cut-off phenomenon in sea surface wind field observation.Wave spectrometers,while unaffected by the azimuth cutoff phenomenon,struggle with low azimuth resolution,impacting the capture of detailed wave and wind field data.This study utilizes SAR and surface wave investigation and monitoring(SWIM)data to initially extract key feature parameters,which are then prioritized using the extreme gradient boosting(XGBoost)algorithm.The research further addresses feature collinearity through a combined analysis of feature importance and correlation,leading to the development of an inversion model for wave and wind parameters based on XGBoost.A comparative analysis of this model with ERA5 reanalysis and buoy data for of significant wave height,mean wave period,wind direction,and wind speed reveals root mean square errors of 0.212 m,0.525 s,27.446°,and 1.092 m/s,compared to 0.314 m,0.888 s,27.698°,and 1.315 m/s from buoy data,respectively.These results demonstrate the model’s effective retrieval of wave and wind parameters.Finally,the model,incorporating altimeter and scatterometer data,is evaluated against SAR/SWIM single and dual payload inversion methods across different wind speeds.This comparison highlights the model’s superior inversion accuracy over other methods.