A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

Sum-Rate Maximization in Active RIS-Assisted Multi-Antenna WPCN

In this paper,we propose an active reconfigurable intelligent surface(RIS)enabled hybrid relaying scheme for a multi-antenna wireless powered communication network(WPCN),where the active RIS is employed to assist both wireless energy transfer(WET)from the power station(PS)to energyconstrained users and wireless information transmission(WIT)from users to the receiving station(RS).For further performance enhancement,we propose to employ both transmit beamforming at the PS and receive beamforming at the RS.We formulate a sumrate maximization problem by jointly optimizing the RIS phase shifts and amplitude reflection coefficients for both the WET and the WIT,transmit and receive beamforming vectors,and network resource allocation.To solve this non-convex problem,we propose an efficient alternating optimization algorithm with the linear minimum mean squared error criterion,semidefinite relaxation(SDR)and successive convex approximation techniques.Specifically,the tightness of applying the SDR is proved.Simulation results demonstrate that our proposed scheme with 10 reflecting elements(REs)and 4 antennas can achieve 17.78%and 415.48%performance gains compared to the single-antenna scheme with 10 REs and passive RIS scheme with 100 REs,respectively.

Maximal operators of pseudo-differential operators with rough symbols

Consider a pseudo-differential operator T_(a)f(x)=∫_(R^(n))e^(ix,ζ)a(x,ζ)f(ζ)dζwhere the symbol a is in the rough Hormander class L^(∞)S_(ρ)^(m)with m∈R andρ∈[0,1].In this note,when 1≤p≤2,if n(ρ-1)/p and a∈L^(∞)S_(ρ)^(m),then for any f∈S(R^(n))and x∈R^(n),we prove that M(T_(a)f)(x)≤C(M(|f|^(p))(x))^(1/p) where M is the Hardy-Littlewood maximal operator.Our theorem improves the known results and the bound on m is sharp,in the sense that n(ρ-1)/p can not be replaced by a larger constant.

MAXIMAL FUNCTION CHARACTERIZATIONS OF HARDY SPACES ASSOCIATED WITH BOTH NON-NEGATIVE SELF-ADJOINT OPERATORS SATISFYING GAUSSIAN ESTIMATES AND BALL QUASI-BANACH FUNCTION SPACES

Assume that L is a non-negative self-adjoint operator on L^(2)(ℝ^(n))with its heat kernels satisfying the so-called Gaussian upper bound estimate and that X is a ball quasi-Banach function space onℝ^(n) satisfying some mild assumptions.Let HX,L(ℝ^(n))be the Hardy space associated with both X and L,which is defined by the Lusin area function related to the semigroup generated by L.In this article,the authors establish various maximal function characterizations of the Hardy space HX,L(ℝ^(n))and then apply these characterizations to obtain the solvability of the related Cauchy problem.These results have a wide range of generality and,in particular,the specific spaces X to which these results can be applied include the weighted space,the variable space,the mixed-norm space,the Orlicz space,the Orlicz-slice space,and the Morrey space.Moreover,the obtained maximal function characterizations of the mixed-norm Hardy space,the Orlicz-slice Hardy space,and the Morrey-Hardy space associated with L are completely new.

Determination of Total Flavonoids in Acanthopanax senticosus(Rupr. et Maxim.) Harms by Enzymolysis and Its Technological Optimization

In order to optimize the ultrasonic extraction technique for the total flavonoid of leaf yellows plus, the contents of 21 leaf yellows plus total flavonoid from four regions in Heilongjiang Province were comparatively analyzed. The ultrasonic extraction technology was optimized by Box-Behnken response surface method, and the total flavonoid content of 21 kinds of Acanthopanax senticosus(Rupr. et Maxim.) Harms from different producing areas were analyzed by cluster analysis. The optimal process conditions were determined as ultrasonic time 30 min, solid-liquid ratio 1 : 12 and ultrasonic power 250 W, and the average yield of the total flavonoid was 1.453 mg·g^ (-1). By optimizing the ultrasonic-assisted extraction method, the total flavonoid content from different producing areas was compared in the experiment, which provided certain data support for the optimization of the extraction process in the future and laid a certain theoretical foundation for the quality analysis of Chinese medicinal materials.

Screening for Anti-tumor Activity of Fractions from Buddleja officinalis Maxim

[Objectives]The anti-tumor activity of fractions from Buddleja officinalis Maxim.by petroleum ether,ethyl acetate,n-butanol and water solvent was studied.[Methods]The ethanol extract from B.officinalis Maxim.was extracted and then concentrated with petroleum ether,ethyl acetate,n-butanol and water,respectively,and the extracts were obtained.The inhibitory effects of the four different fractions on the growth of three tumor cell lines in vitro were detected by CCK-8 method,and the median inhibitory concentration(IC 50 value)was calculated.[Results]The four fractions inhibited the growth of the three tumor cell lines in vitro,among which the n-butanol fraction had the best anti-tumor activity.The IC 50 values of the n-butanol fraction on human gastric cancer(SGC-7901),human breast cancer(MCF-7)and human liver cancer(BEL-7404)cell lines were 0.08,1.58 and 0.12 mg/mL,respectively.[Conclusions]Petroleum ether,ethyl acetate,n-butanol and water fractions from the ethanol extract of B.officinalis Maxim.had certain anti-tumor effects,and the n-butanol fraction had the best anti-tumor activity.

An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

Influence maximization of temporal social networks(IMT)is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread.To solve the IMT problem,we propose an influence maximization algorithm based on an improved K-shell method,namely improved K-shell in temporal social networks(KT).The algorithm takes into account the global and local structures of temporal social networks.First,to obtain the kernel value Ks of each node,in the global scope,it layers the network according to the temporal characteristic of nodes by improving the K-shell method.Then,in the local scope,the calculation method of comprehensive degree is proposed to weigh the influence of nodes.Finally,the node with the highest comprehensive degree in each core layer is selected as the seed.However,the seed selection strategy of KT can easily lose some influential nodes.Thus,by optimizing the seed selection strategy,this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization(KTIM).According to the hierarchical distribution of cores,the algorithm adds nodes near the central core to the candidate seed set.It then searches for seeds in the candidate seed set according to the comprehensive degree.Experiments showthatKTIMis close to the best performing improved method for influence maximization of temporal graph(IMIT)algorithm in terms of effectiveness,but runs at least an order of magnitude faster than it.Therefore,considering the effectiveness and efficiency simultaneously in temporal social networks,the KTIM algorithm works better than other baseline algorithms.

Topology Driven Cooperative Self Scheduling for Improved Lifetime Maximization in WSN

In Wireless Sensor Network(WSN),scheduling is one of the important issues that impacts the lifetime of entire WSN.Various scheduling schemes have been proposed earlier to increase the lifetime of the network.Still,the results from such methods are compromised in terms of achieving high lifetime.With this objective to increase the lifetime of network,an Efficient Topology driven Cooperative Self-Scheduling(TDCSS)model is recommended in this study.Instead of scheduling the network nodes in a centralized manner,a combined approach is proposed.Based on the situation,the proposed TDCSS approach performs scheduling in both the ways.By sharing the node statistics in a periodic manner,the overhead during the transmission of control packets gets reduced.This in turn impacts the lifetime of all the nodes.Further,this also reduces the number of idle conditions of each sensor node which is required for every cycle.The proposed method enables every sensor to schedule its own conditions according to duty cycle and topology constraints.Central scheduler monitors the network conditions whereas total transmissions occurs at every cycle.According to this,the source can infer the possible routes in a cycle and approximate the available routes.Further,based on the statistics of previous transmissions,the routes towards the sink are identified.Among the routes found,a single optimal route with energy efficiency is selected to perform data transmission.This cooperative approach improves the lifetime of entire network with high throughput performance.

The study of a neutron spectrum unfolding method based on particle swarm optimization combined with maximum likelihood expectation maximization

The neutron spectrum unfolding by Bonner sphere spectrometer(BSS) is considered a complex multidimensional model,which requires complex mathematical methods to solve the first kind of Fredholm integral equation. In order to solve the problem of the maximum likelihood expectation maximization(MLEM) algorithm which is easy to suffer the pitfalls of local optima and the particle swarm optimization(PSO) algorithm which is easy to get unreasonable flight direction and step length of particles, which leads to the invalid iteration and affect efficiency and accuracy, an improved PSO-MLEM algorithm, combined of PSO and MLEM algorithm, is proposed for neutron spectrum unfolding. The dynamic acceleration factor is used to balance the ability of global and local search, and improves the convergence speed and accuracy of the algorithm. Firstly, the Monte Carlo method was used to simulated the BSS to obtain the response function and count rates of BSS. In the simulation of count rate, four reference spectra from the IAEA Technical Report Series No. 403 were used as input parameters of the Monte Carlo method. The PSO-MLEM algorithm was used to unfold the neutron spectrum of the simulated data and was verified by the difference of the unfolded spectrum to the reference spectrum. Finally, the 252Cf neutron source was measured by BSS, and the PSO-MLEM algorithm was used to unfold the experimental neutron spectrum.Compared with maximum entropy deconvolution(MAXED), PSO and MLEM algorithm, the PSO-MLEM algorithm has fewer parameters and automatically adjusts the dynamic acceleration factor to solve the problem of local optima. The convergence speed of the PSO-MLEM algorithm is 1.4 times and 3.1 times that of the MLEM and PSO algorithms. Compared with PSO, MLEM and MAXED, the correlation coefficients of PSO-MLEM algorithm are increased by 33.1%, 33.5% and 1.9%, and the relative mean errors are decreased by 98.2%, 97.8% and 67.4%.

Maximizing Influence in Temporal Social Networks:A Node Feature-Aware Voting Algorithm

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

DeepWalk Based Influence Maximization (DWIM): Influence Maximization Using Deep Learning

Big Data and artificial intelligence are used to transform businesses.Social networking sites have given a new dimension to online data.Social media platforms help gather massive amounts of data to reach a wide variety of customers using influence maximization technique for innovative ideas,products and services.This paper aims to develop a deep learning method that can identify the influential users in a network.This method combines the various aspects of a user into a single graph.In a social network,the most influential user is the most trusted user.These significant users are used for viral marketing as the seeds to influence other users in the network.The proposed method combines both topical and topological aspects of a user in the network using collaborativefiltering.The proposed method is DeepWalk based Influence Maximization(DWIM).The proposed method was able tofind k influential nodes with computable time using the algorithm.The experiments are performed to assess the proposed algorithm,and centrality measures are used to compare the results.The results reveal its performance that the proposed method canfind k influential nodes in computable time.DWIM can identify influential users,which helps viral marketing,outlier detection,and recommendations for different products and services.After applying the proposed methodology,the set of seed nodes gives maximum influence measured with respect to different centrality measures in an increased computable time.

Study of Optimal Frequency to the Repairable System Due to Failure Finding Interval to Maximize Availability System

This research article is based on a study of optimal frequency to the repairable system due to the failure finding interval to maximize as well as minimize the availability of some components devices. We studied together maintenance and corrective actions that carried out item of failure and periodic failure finding designed to check whether a system is still working. The model is proved as well as useful application in detecting the problem related to finding failure tasks of different scheme devices by maximization. The model formulated and the numerical application to the relevant mathematical model have been discussed to demonstrate the article quality. Therefore based on probability analytic development, the optimal maintenance policy is then obtained as solution of an optimization problem in which the maintenance cost rate is the objective function and the risk of corrective maintenance is the constraint function. Finally, the solution to the optimal device in the considered development model has been well adjusted due to derivation to the experimental observation rather than theory which will be taken into consideration in the next applied practical design research related and the system device provided that, the proactive device agreed with using the exponential distribution to the survive distribution function which can not be considered as valid.

Model-Free Feature Screening via Maximal Information Coefficient (MIC) for Ultrahigh-Dimensional Multiclass Classification

It is common for datasets to contain both categorical and continuous variables. However, many feature screening methods designed for high-dimensional classification assume that the variables are continuous. This limits the applicability of existing methods in handling this complex scenario. To address this issue, we propose a model-free feature screening approach for ultra-high-dimensional multi-classification that can handle both categorical and continuous variables. Our proposed feature screening method utilizes the Maximal Information Coefficient to assess the predictive power of the variables. By satisfying certain regularity conditions, we have proven that our screening procedure possesses the sure screening property and ranking consistency properties. To validate the effectiveness of our approach, we conduct simulation studies and provide real data analysis examples to demonstrate its performance in finite samples. In summary, our proposed method offers a solution for effectively screening features in ultra-high-dimensional datasets with a mixture of categorical and continuous covariates.

驾驶疲劳对危险化学品道路运输事故风险的影响规律

近年来,随着危险化学品使用量的急剧攀升,危险化学品道路运输事故率也呈现上升的趋势,且此类事故的发生往往会导致严重后果。为研究危险化学品道路运输事故动态风险变化规律,在修正贝叶斯网络模型基础上,利用2017—2021年历史数据进行机器学习,根据驾驶疲劳程度计算得到“驾驶人行为”动态节点的状态转移概率矩阵,建立基于动态贝叶斯网络(Dynamic Bayesian Network,DBN)的危险化学品道路运输动态风险预测模型并进行推理分析。研究显示:在驾驶3 h内,驾驶人“疲劳驾驶”发生概率随时间推移而增加,但增幅有所下降;在最常见情境下,随驾驶人“疲劳驾驶”概率增加,“侧翻”和“碰撞”事故类型的发生概率明显增加,进而导致“泄漏”事故后果的发生概率有所增加;驾驶人“疲劳驾驶”概率增加会导致“有伤亡事故”发生概率增加,即加重事故的严重程度;在驾驶3 h内,“侧翻”“碰撞”“泄漏”和“有伤亡事故”发生概率的变化趋势与驾驶人“疲劳驾驶”发生概率的变化趋势一致。

外来植物资源土圞儿健康价值挖掘及产业化前景展望

土圞儿是一种外来资源植物,具有较高的经济和健康价值。然而,与国外相比,国内对土圞儿的开发和利用研究较少,在其种质资源培育、生产加工技术、产品开发及市场开拓等方面缺乏相关经验。通过对文献资料和专利数据的全面分析,深入剖析土圞儿的研究现状、国内外种植业的发展情况,以及其健康价值和应用前景。针对性地提出土圞儿产业绿色健康发展对策,构建其资源循环利用产业链,旨在为土圞儿资源的深入开发利用提供参考、拓宽其应用领域,为政策制定和产业规划提供依据,进一步推动其产业转型升级和可持续绿色健康发展。

硬件损伤条件下无人机辅助的NOMA-IRS网络资源分配算法

探讨了在硬件损伤条件下,如何通过搭载在无人机上的智能反射面(IRS)有效支持基于非正交多址接入(NOMA)技术的网络,以加快多用户数据的传输速率。提出了一种全面优化策略实现整个通信系统的最大传输速率,包括联合调整串行干扰消除的解码策略、IRS的反射配置、无人机的空间位置以及基站的发射功率。由于该问题是一个非凸问题,提出使用块坐标下降方法来实现优化的一种迭代算法,将原始的非凸问题细分为3个子问题,并通过结合惩罚函数法、半正定松弛技术和连续凸近似法来解决这些子问题。仿真结果表明:提出的算法在系统总速率方面相比随机部署无人机、未使用IRS辅助的NOMA策略,系统速率有显著提升。

基于EM-KF算法的微地震信号去噪方法

针对微地震信号能量较弱,噪声较强,使微地震弱信号难以提取问题,提出了一种基于EM-KF(Expectation Maximization Kalman Filter)的微地震信号去噪方法。通过建立一个符合微地震信号规律的状态空间模型,并利用EM(Expectation Maximization)算法获取卡尔曼滤波的参数最优解,结合卡尔曼滤波,可以有效地提升微地震信号的信噪比,同时保留有效信号。通过合成和真实数据实验结果表明,与传统的小波滤波和卡尔曼滤波相比,该方法具有更高的效率和更好的精度。

秋子梨果实矿质元素含量差异分析

以黑龙江省36份秋子梨果实为试材,采用ICP-MS法对果皮和果肉中12种矿质元素含量进行测定并比较分析。结果表明:秋子梨果实中各矿质元素的平均含量依次为K>P>Ca>Mg>Na>Al>Fe>Zn>Cu>Cr>Pb>Cd,不同种质中果皮和果肉各元素的含量差异明显,野生资源的果实矿质元素含量高于选育品种、农家品种和优良品系。相关性分析结果表明,果皮和果肉中的P、K、Fe、Cu和Zn这5种元素之间存在极显著正相关。聚类分析结果表明,根据果皮或果肉的矿质元素含量,36份秋子梨种质可分为2个略有不同的类别。主成分分析表明,S23、S20、S16和S10是果实矿质元素总含量较高的品种,可优先作为功能性品种选育种质材料,为秋子梨果实矿质元素的遗传改良提供更为深入和有价值的依据。

基于改进信息最大化生成对抗网络的风光出力场景可控生成方法

基于深度学习的场景生成方法能够自适应挖掘历史数据中高维非线性特征,在风光出力的不确定性建模中得到了广泛应用。然而,基于深度学习的场景生成方法多为黑盒模型,存在可解释性差、生成不可控等问题。为此,提出了一种基于改进信息最大化生成对抗网络(information maximizing generative adversarial nets,Info GAN)的风光出力场景生成方法。该方法在目标函数中增加了基于互信息的正则化项,最大化控制编码与生成场景之间的互信息,无监督学习控制编码与生成场景统计特征的映射关系,并引入Gumbel-Softmax分布提高了生成场景的质量。结合风电场和光伏电站的真实数据进行了算例分析,算例结果表明,所提方法不仅能准确描述风光出力不确定性,而且具有可解释性,能够可控生成指定风光出力场景。

花椒病虫害绿色防控技术研究进展

花椒(Zanthoxylum bungeanum Maxim.)经济价值较高,在中国广泛种植,但其生长过程中易遭受病虫危害,科学的防治技术措施对花椒的绿色发展至关重要。从生态调控技术、生物防治技术、理化诱控技术、科学用药技术4个方面对花椒病虫害绿色防控技术进行综述,以期为生产上花椒病虫害绿色防控提供参考。