Detection of Insider Selective Forwarding Attack Based on Monitor Node and Trust Mechanism in WSN

The security problems of wireless sensor networks (WSN) have attracted people’s wide attention. In this paper, after we have summarized the existing security problems and solutions in WSN, we find that the insider attack to WSN is hard to solve. Insider attack is different from outsider attack, because it can’t be solved by the traditional encryption and message authentication. Therefore, a reliable secure routing protocol should be proposed in order to defense the insider attack. In this paper, we focus on insider selective forwarding attack. The existing detection mechanisms, such as watchdog, multipath retreat, neighbor-based monitoring and so on, have both advantages and disadvantages. According to their characteristics, we proposed a secure routing protocol based on monitor node and trust mechanism. The reputation value is made up with packet forwarding rate and node’s residual energy. So this detection and routing mechanism is universal because it can take account of both the safety and lifetime of network. Finally, we use OPNET simulation to verify the performance of our algorithm.

Adaptive multi-channel Bayesian graph attention network for IoT transaction security

With the rapid advancement of 5G technology,the Internet of Things(IoT)has entered a new phase of appli-cations and is rapidly becoming a significant force in promoting economic development.Due to the vast amounts of data created by numerous 5G IoT devices,the Ethereum platform has become a tool for the storage and sharing of IoT device data,thanks to its open and tamper-resistant characteristics.So,Ethereum account security is necessary for the Internet of Things to grow quickly and improve people's lives.By modeling Ethereum trans-action records as a transaction network,the account types are well identified by the Ethereum account classifi-cation system established based on Graph Neural Networks(GNNs).This work first investigates the Ethereum transaction network.Surprisingly,experimental metrics reveal that the Ethereum transaction network is neither optimal nor even satisfactory in terms of accurately representing transactions per account.This flaw may significantly impede the classification capability of GNNs,which is mostly governed by their attributes.This work proposes an Adaptive Multi-channel Bayesian Graph Attention Network(AMBGAT)for Ethereum account clas-sification to address this difficulty.AMBGAT uses attention to enhance node features,estimate graph topology that conforms to the ground truth,and efficiently extract node features pertinent to downstream tasks.An extensive experiment with actual Ethereum transaction data demonstrates that AMBGAT obtains competitive performance in the classification of Ethereum accounts while accurately estimating the graph topology.

PREDICTION OF TEXTILE FABRIC REINFORCED COMPOSITE PROPERTIES BASED ON NODE INTERPOLATION CELL METHOD

Node interpolation cell method（NICM）is a micromechanics method employing the virtual displacement principle and the representative volume element（RVE）scheme to obtain the relationship between the global and the local strain.Mechanical properties of 2-D textile fabric reinforced ceramic matrix composites are predicted by NICM.Microstructures of 2-D woven and braided fabric reinforced composite are modeled by two kinds of RVE scheme.NICM is used to predict the macroscopic mechanical properties.The fill and warp yarns are simulated with cubic B-spline and their undulating forms are approximated by sinusoid.The effect of porosity on the fiber and matrix are considered as a reduction of elastic module.The connection of microstructure parameters and fiber volume fraction is modeled to investigate the reflection on the mechanical properties.The results predicted by NICM are compared with that by the finite element method（FEM）.The comparison shows that NICM is a valid and feasible method for predicting the mechanics properties of 2-D woven and braided fabric reinforced ceramic matrix composites.

Comparison of efficacy and safety between late-course and simultaneous integrated dose-increasing intensity-modulated radiation therapy for cervical cancer complicated with pelvic lymph node metastasis

Objective This study aimed to compare and analyze the clinical efficacy and safety of late-course and simultaneous integrated dose-increasing intensity-modulated radiation therapy(IMRT) for cervical cancer complicated with pelvic lymph node metastasis. Methods Sixty patients with cervical cancer complicated with pelvic lymph node metastasis who were admitted to our hospital from January 2013 to January 2015 were enrolled. The patients were randomly divided into the late-course dose-increasing IMRT group and the simultaneous integrated dose-increasing IMRT group, with 30 cases included in each group, respectively. All patients were concurrently treated with cisplatin. After treatment, the clinical outcomes of the two groups were compared. Results The remission rate of symptoms in the simultaneous integrated dose-increasing IMRT group was significantly higher than that in the late-course dose-increasing IMRT group(P < 0.05). The follow-up results showed that the overall survival time, progression-free survival time, and distant metastasis time of patients in the simultaneous integrated dose-increasing IMRT group were significantly longer than those in the late-course dose-increasing IMRT group(P < 0.05). The recurrent rate of lymph nodes in the radiation field in the simultaneous integrated dose-increasing IMRT group was significantly lower(P < 0.05) than in the late-course dose-increasing IMRT group. There was no significant difference in the incidence of cervical and vaginal recurrence and distant metastasis between the two groups(P > 0.05). The radiation doses of Dmax in the small intestine, D1 cc(the minimum dose to the 1 cc receiving the highest dose) in the bladder, and Dmax in the rectum in the simultaneous integrated dose-increasing IMRT group were significantly lower(P < 0.05) than in the late-course dose-increasing IMRT group. There was no significant difference in intestinal D2 cc(the minimum dose to the 2 cc receiving the highest dose) between the two groups(P > 0.05). The incidence of bone marrow suppression in the simultaneous integrated dose-increasing IMRT group was significantly lower(P < 0.05) than in the late-course dose-increasing IMRT group.Conclusion The application of simultaneous integrated dose-increasing IMRT in the treatment of cervical cancer patients complicated with pelvic lymph node metastasis can significantly control tumor progression, improve the long-term survival time, and postpone distant metastasis time with high safety.

Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

In recent scenario of Wireless Sensor Networks(WSNs),there are many application developed for handling sensitive and private data such as military information,surveillance data,tracking,etc.Hence,the sensor nodes of WSNs are distributed in an intimidating region,which is non-rigid to attacks.The recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and threats.In traditional models,the solution has been made for defending the networks,only to specific attacks.However,in real-time applications,the kind of attack that is launched by the adversary is not known.Additionally,on developing a security mechanism for WSN,the resource constraints of sensor nodes are also to be considered.With that note,this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism(IDRM)for defending the sensor network from various attacks.Moreover,for efficient model design,the work includes the part of feature evaluation of some general attacks of WSNs.The IDRM also includes determination of optimal secure paths and Node security for secure routing operations.The performance of the proposed model is evaluated with respect to several factors;it is found that the model has achieved better security levels and is efficient than other existing models in WSN communications.It is proven that the proposed IDRM produces 74%of PDR in average and a minimized packet drop of 38%when comparing with the existing works.

A Secure Microgrid Data Storage Strategy with Directed Acyclic Graph Consensus Mechanism

The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years.In real-world scenarios,microgrids must store large amounts of data efficiently while also being able to withstand malicious cyberattacks.To meet the high hardware resource requirements,address the vulnerability to network attacks and poor reliability in the tradi-tional centralized data storage schemes,this paper proposes a secure storage management method for microgrid data that considers node trust and directed acyclic graph(DAG)consensus mechanism.Firstly,the microgrid data storage model is designed based on the edge computing technology.The blockchain,deployed on the edge computing server and combined with cloud storage,ensures reliable data storage in the microgrid.Secondly,a blockchain consen-sus algorithm based on directed acyclic graph data structure is then proposed to effectively improve the data storage timeliness and avoid disadvantages in traditional blockchain topology such as long chain construction time and low consensus efficiency.Finally,considering the tolerance differences among the candidate chain-building nodes to network attacks,a hash value update mechanism of blockchain header with node trust identification to ensure data storage security is proposed.Experimental results from the microgrid data storage platform show that the proposed method can achieve a private key update time of less than 5 milliseconds.When the number of blockchain nodes is less than 25,the blockchain construction takes no more than 80 mins,and the data throughput is close to 300 kbps.Compared with the traditional chain-topology-based consensus methods that do not consider node trust,the proposed method has higher efficiency in data storage and better resistance to network attacks.

胜利油田陆相页岩油前置CO_(2)压裂技术研究与实践

陆相页岩油是我国油气资源重要的产量接替阵地,胜利油田济阳坳陷页岩油是我国陆相断陷盆地页岩油的典型代表,预测资源量超百亿吨,勘探开发前景广阔。但与北美海相页岩油的区别在于,济阳页岩油具有埋藏深、成熟度低等特点,压裂工艺造缝及缝网导流面临更大挑战。前置CO_(2)压裂技术集成了CO_(2)干法压裂和常规水力压裂的优势,在强化陆相页岩油增产效果方面成效显著。以胜利油田济阳坳陷陆相页岩油为例,开展前置CO_(2)+压裂液压裂研究,通过流体高压物性实验、岩石力学实验、真三轴压裂实验和数值模拟等多种研究手段相结合,揭示了前置CO_(2)压裂过程中复杂的水/岩和多相流体作用机制,明确其在降低储层破裂压力、改善储层物性、提高储/地层能量、促进复杂缝网形成、降低储层伤害和提高页岩油流动性等方面的作用效果。在理论研究基础上,结合矿场试验,阐明了前置CO_(2)压裂在陆相页岩油高效开发中的技术优势和应用前景,总结了技术存在问题,预测了下步发展趋势,为进一步提高我国陆相页岩油开发效果、实现CO_(2)高效埋存和利用提供了指导和借鉴。

基于Raft的多主节点拜占庭容错共识机制

为了解决联盟链中实用拜占庭容错(PBFT)共识机制在区块链网络中节点数量增多的情况下,通信复杂度高、共识效率低下等问题,本文提出一种基于Raft的多主节点拜占庭容错共识机制IMRBFT。IMRBFT通过Maglev一致性哈希算法对区块链网络节点均匀分组,将这个共识流程分成组外共识和组内共识2部分。组内先选出领导者节点,通过信用机制将节点分为3个等级:可信节点、普通节点和不可信节点。与投票机制共同降低恶意节点成为领导者节点的概率,并与其他组的领导者节点组成委员会,委员会再经过组外信用值机制选出信用值最高的多个主节点进行组外PBFT共识。组内共识在Raft共识的基础上引入监管节点与中继节点,进一步提升安全性与共识效率,减少恶意节点的作恶行为。实验结果表明:IMRBFT的通信开销为线性增长,通信量为PBFT的41.6%,吞吐量为PBFT的4.2倍,共识延时降低76.4%。随着节点增多,优化更加明显,完全满足大型区块链网络的通信复杂度小、吞吐量高、共识延时短、安全性与共识效率高的要求。

加气混凝土复合保温外墙板连接节点试验研究

通过对钢管锚节点、套筒节点和新型F型节点进行的6组加气混凝土板四分点加载节点极限承载力试验,分析不同板厚下3种节点的节点强度、节点刚度等性能指标。研究表明:新型F型节点力学性能良好,连接可靠,节点极限承载力随板厚增加而增大。新型F型节点可较好地适用在加气混凝土复合保温外墙板体系中。

基于深度学习的宫颈癌淋巴结转移预测

目的:基于深度学习,利用T_(2)加权成像(T_(2)WI)序列的高分辨特性获得宫颈癌淋巴结的结构信息,并预测淋巴结是否转移;利用弥散加权成像(DWI)序列的功能特性,获取淋巴结区域,并预测淋巴结是否转移;综合多模态MRI数据,预测淋巴结是否转移。方法:收集2021年6月至2022年5月年新疆维吾尔自治区人民医院收治的52例宫颈癌患者的多参数MRI影像数据以及病理检查数据作为训练集,另收集2022年6月至2023年5月新疆维吾尔自治区人民医院收治的150例宫颈癌患者多参数MRI影像数据以及病理检查数据作为验证集。训练集52例宫颈癌患者均接受MRI扫描,扫描序列包括T_(2)WI和DWI序列。对训练集52例宫颈癌患者的多参数MRI影像学图像进行非均匀性校正和标准化的预处理后,通过渐进演化空洞卷积对T_(2)WI图像进行分割,在扩大感受野的同时,有效降低空洞对图像丢失的影响;通过基于注意力网络机制的深度学习模型引导网络在预测时更关注淋巴结区域,并为预测结果提供一定程度的解释性;通过多模态协同学习模型实现T_(2)WI和DWI序列在淋巴结性质预测任务之间的经验共享。采用验证集患者的图像资料对基于多模态协同学习模型的淋巴结转移预测模型进行验证。结果:验证集150例患者中良性淋巴结585枚,恶性淋巴结65枚,其良恶性淋巴结在大小(长径、短径)和边界上存在差异,差异有统计学意义(x^(2)=8.437、143.100、104.608,P<0.05)。验证集150例患者中48例患者出现淋巴结转移,基于多模态协同学习模型的淋巴结性质预测模型准确预测出46例患者出现淋巴结转移,准确预测出99例患者未发生淋巴结转移,预测准确率为96.67%。结论:渐进演化空洞卷积结合U-Net框架完成了对T_(2)WI宫颈癌影像的多目标自动分割任务,基于注意力网络机制的深度学习模型完成了动态关注淋巴结区域的任务,多模态协同学习模型有效地避免了特征层融合之后特征空间分布的改变。

面向分布式电力交易的实用拜占庭容错共识算法改进

针对实用拜占庭容错(Practical Byzantine fault tolerance,PBFT)共识算法在分布式电力交易场景下共识时存在的主节点选取策略简单、缺少奖惩措施以及通信资源消耗较大等问题,提出一种改进实用拜占庭容错共识算法。首先,明确分布式电力交易(Distributed power trading,DPT)参与方构成,制定参与方对应节点在共识过程中应承担的权责;其次,制定节点信誉值计算方法,根据节点在共识中的表现筛选不良行为节点,有效限制拜占庭节点参与共识;此外,基于信誉值与散列算法实现了一种可验证的主节点选取方法,使主节点选取难以预测;最后,引入多主节点改进一致性协议,提高算法对拜占庭主节点的容忍程度。实验结果表明,DPT-PBFT算法在通信开销、共识时延等方面相较于PBFT算法均有不同程度的提升,能够有效适用于分布式电力交易场景。

乡村振兴背景下焦作市农民增收长效机制建设研究

党的二十大报告提出,全面建设社会主义现代化国家,最艰巨最繁重的任务仍然在农村。2023年中央一号文件提出拓宽农民增收致富渠道,全面推进乡村振兴。围绕当前农民持续增收痛点难点,对焦作市农村农民收入现状进行深入研究,分析农民增收困境,提出构建农民增收长效机制的对策和建议。

新型煤矿单轨吊H型钢-轨道连接子结构受力性能研究

煤矿单轨吊辅助运输是保证矿井可持续发展和提高煤炭运输效率的关键性设备。根据不完全的数据统计,每年我国煤炭需运输约200亿t的物资和设备,其中有四分之一以上是依靠单轨吊的辅助运输进行的,但每年总会出现吊车倾覆、悬挂点断裂等事故。不断改进对单轨吊轨道的悬挂形式,以改善其安装质量,这对于矿井单轨吊辅助运输设备有着十分重要的作用。基于此,介绍了一种新型煤矿单轨吊H型钢梁-轨道连接的悬挂形式,并对节点子结构的实体单元进行了有限元分析,以此来研究其受力性能。

基于无线传感器网络的鱼塘水质管理研究

随着水产养殖业的快速发展,鱼塘水质管理成为保障养殖效率与产品品质的关键因素。文章探讨了一种基于无线传感器网络的鱼塘水质监测系统的设计与应用。该系统集成了多种水质传感器,如溶解氧、pH值、温度和氨氮浓度传感器,以实时监测鱼塘的水质参数。传感器节点通过无线方式将采集到的数据传输至汇聚节点,再由汇聚节点将数据发送至远程监控中心,实现对水质的远程监控与管理。该系统采用自组织网络技术,能够适应鱼塘复杂多变的环境,降低布线成本,提高监测灵活性。此外,该研究还涉及数据处理算法与预警机制,以便及时发现水质异常并采取相应措施,确保水质维持在适宜养殖的范围内。通过试验验证,该系统有效提升了鱼塘水质管理的效率与精确度,对促进水产养殖业的智能化发展具有重要意义。

竹节结构及力学性能研究现状

竹材优异的柔韧性有利于其塑性加工应用,但在竹材展平或塑性弯曲成型时常常出现开裂现象,尤其在竹节位置,严重影响成品质量。在现行的实验标准中未对竹节处加工进行特别规定,忽略了竹节结构的特殊性及其对竹材整体性能的影响。文章分析了竹节特殊的形态结构、力学性能及其对竹质板材及仿生材料的影响,对比了竹节与节间在结构及力学性能方面的差异,旨在探索更高效的竹材加工方式,提高竹质板材的成品率,为竹质板材的结构设计以及竹材塑性加工提供理论参考。分析显示,竹节与节间在形态结构及力学性能方面均有明显差异,竹节处交错的水平维管束和垂直维管束提高了竹材整体的横向抗拉强度、抗弯强度和抗压强度,增强了竹材纵向承载力和横向稳定性;但对于锯切后的竹条单元,则在一定程度上降低了其顺纹抗压、抗弯和抗拉强度。

聚乳酸聚氨酯嵌段预聚体增韧PLA/TPU共混物

合成聚乳酸聚氨酯嵌段共聚物预聚体(PLA-b-PUP),以其作为聚乳酸(PLA)和热塑性聚氨酯(TPU)的活性相容剂,通过原位反应增容制备PLA/TPU/PLA-b-PUP超韧共混物.通过拉伸试验、冲击试验、SEM、FTIR、DSC和TGA研究共混物的力学性能、热性能和增韧机理.结果表明,PLA-b-PUP中的异氰酸酯基团与PLA和TPU上的活性基团发生反应,显著改善了PLA/TPU共混物两相界面的相容性.随着PLA-b-PUP的加入量增加,共混物中PLA的玻璃化转变温度和相对结晶度逐渐降低,当PLA-b-PUP的质量分数为PLA/TPU共混物的4%时,共混材料的断裂伸长率和缺口冲击强度分别达到无相容剂时的8.12和2.73倍,表现出良好的增容增韧效果.添加PLA-b-PUP后,共混物的初始分解温度有所降低,但最快分解温度有所提高.

NLGAE:一种基于改进网络结构及损失函数的图自编码器节点分类模型

利用图嵌入方法将图的拓扑结构、节点属性等高维异构信息映射到稠密的向量空间,是解决图数据由非欧空间性带来的计算不友好、邻接矩阵的高度空间复杂性等问题的主流方法。在对经典图自编码器模型GAE与VGAE所存在的问题进行分析的基础上,尝试从编码器、解码器及损失函数3个方面对基于图自编码器的图嵌入方法进行改进,提出一种基于改进网络结构及损失函数的图自编码器模型NLGAE。首先,在模型结构设计上,一方面将编码器中堆叠的图卷积层倒置,以解决GAE与VGAE中无参Decoder缺乏灵活性并且表达能力不足的问题,另一方面引入注意力机制的图卷积网络GAT来解决节点之间的权重系数固化的问题;其次,重新设计的损失函数能够同时考虑到图结构与节点特征属性两部分信息。对比实验结果表明:NLGAE作为一种无监督模型,能够学习到高质量的节点嵌入特征,在下游节点分类任务上优于DeepWalk,GAE,GrpahMAE,GATE等经典无监督模型,并且在选择合适分类模型的情况下,甚至优于GAT和GCN等有监督的图神经网络模型。

日志信息驱动的计算机网络节点故障预测研究

针对计算机网络中节点故障对正常业务运行的影响,提出了一种以日志信息为驱动的故障预测方法,通过构建高效的深度学习模型,并引入校正机制,对计算机网络中的节点故障进行预测和诊断,支持网络运维的需求。首先收集计算机网络中各节点产生的日志信息,获得各节点的状态向量和所有节点的状态矩阵,然后通过状态填补原则补充数据集,最后将故障预测问题转换成时间序列预测问题。在公开的小规模运维数据集GAIA中进行性能评估。实验结果表明,与其他算法相比,所提模型在局部网络场景下预测效果良好,预测有效性得到了验证,为计算机网络故障预测研究提供了一定的参考价值。

肠道菌群在治疗高尿酸血症中的研究进展

肠道微生物群定植于人体肠道,其结构变化或失衡可参与嘌呤代谢、酶的合成及炎症细胞因子的释放,进而引起代谢紊乱,这与高尿酸血症的发生、发展密切相关。将肠道菌群作为探究高尿酸血症发病机制的切入点,已成为新的研究热点。本文就不同类型的肠道菌群在高尿酸血症发病机制中的研究现状与进展进行综述,总结了肠道菌群在直接减少尿酸生成;增加嘌呤代谢;影响氨基酸转运,以及增加尿酸排泄等方面的具体作用机制,以期为未来治疗高尿酸血症提供新思路。

基于k-means的动态多组织PBFT算法

联盟区块链系统被广泛用于金融和物流等场景。现有应用于区块链系统的实用拜占庭算法(practical Byzantine fault tolerance,PBFT)存在可扩展性较低及通信成本较高等问题,阻碍了区块链系统在大规模场景中的应用。针对上述问题,提出了一种动态多组织实用拜占庭容错算法(k-means-practical Byzantine fault tolerance,k-PBFT)。通过改进k-means算法,根据节点的时延以及节点间通信距离将节点分为多个自治组织,各组织之间通过组织代表节点进行通信。当新节点加入时,根据其特点将其分配到最合理的组织。同时,引入信誉机制以辨别系统中的诚实节点与恶意节点,从而提高系统的安全性。此外,该算法还引入节点任期机制,使区块链中每个诚实节点都有机会充当组织代表节点或主节点。实验结果表明,与PBFT算法相比,k-PBFT算法通信复杂度降低了75%;当节点数为100时,相比于PBFT算法,时延降低了210 ms,吞吐量提高了100%。在高延迟环境下,相较于基于信誉分组的PBFT改进算法,当节点数为100时,时延降低了20%,吞吐量提高了17%。