Deducing Complete Selection Rule Set for Driver Nodes to Guarantee Network’s Structural Controllability

Structural controllability is critical for operating and controlling large-scale complex networks. In real applications, for a given network, it is always desirable to have more selections for driver nodes which make the network structurally controllable. Different from the works in complex network field where structural controllability is often used to explore the emergence properties of complex networks at a macro level,in this paper, we investigate it for control design purpose at the application level and focus on describing and obtaining the solution space for all selections of driver nodes to guarantee structural controllability. In accord with practical applications,we define the complete selection rule set as the solution space which is composed of a series of selection rules expressed by intuitive algebraic forms. It explicitly indicates which nodes must be controlled and how many nodes need to be controlled in a node set and thus is particularly helpful for freely selecting driver nodes. Based on two algebraic criteria of structural controllability, we separately develop an input-connectivity algorithm and a relevancy algorithm to deduce selection rules for driver nodes. In order to reduce the computational complexity,we propose a pretreatment algorithm to reduce the scale of network's structural matrix efficiently, and a rearrangement algorithm to partition the matrix into several smaller ones. A general procedure is proposed to get the complete selection rule set for driver nodes which guarantee network's structural controllability. Simulation tests with efficiency analysis of the proposed algorithms are given and the result of applying the proposed procedure to some real networks is also shown, and these all indicate the validity of the proposed procedure.

RBF-based cluster-head selection for wireless sensor networks

The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.

A Fast Global Node Selection Algorithm for Bearings-only Target Localization

In the target tracking, the nodes aggregate their observations of the directions of arrival of the target. The network then uses an extended Kalman filter （EKF） to combine the measurements from multiple snapshots to track the target. In order to rapidly select the best subset of nodes to localize the target with the minimum mean square position error and low power consumption, this paper proposes a simple algorithm, which uses the location information of the target and the network. The lower botmd of localization error is utilized according to the distances between the target and the selected active nodes. Furthermore, the direction likelihoods of the active nodes is predicted by way of the node/target bearing distributing relationships.

An Area Optimization Based Cooperative Localization Algorithm with Node Selection

In recent years,position information has become a key feature to drive location and context aware services in mobile communication.Researchers from all over the world have proposed many solu-tions for indoor positioning over the past several years.However,due to weak signals,multipath or non-line-of-sight signal propagation,accurately and efficiently localizing targets in harsh indoor environments re-mains a challenging problem.To improve the perfor-mance in harsh environment with insufficient anchors,cooperative localization has emerged.In this paper,a novel cooperative localization algorithm,named area optimization and node selection based sum-product al-gorithm over a wireless network(AN-SPAWN),is de-scribed and analyzed.To alleviate the high compu-tational complexity and build optimized cooperative cluster,a node selection method is designed for the cooperative localization algorithm.Numerical experi-ment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional cooperative localization algo-rithms in the harsh indoor environments.

Management of lateral pelvic lymph nodes in rectal cancer:Is it time to reach an Agreement?

In this editorial,we proceed to comment on the article by Chua et al,addressing the management of metastatic lateral pelvic lymph nodes(mLLN)in stage II/III rectal cancer patients below the peritoneal reflection.The treatment of this nodal area sparks significant controversy due to the strategic differences followed by Eastern and Western physicians,albeit with a higher degree of convergence in recent years.The dissection of lateral pelvic lymph nodes without neoadjuvant therapy is a standard practice in Eastern countries.In contrast,in the West,preference leans towards opting for neoadjuvant therapy with chemoradiotherapy or radiotherapy,that would cover the treatment of this area without the need to add the dissection of these nodes to the total mesorectal excision.In the presence of high-risk nodal characteristics for mLLN related to radiological imaging and lack of response to neoadjuvant therapy,the risk of lateral local recurrence increases,suggesting the appropriate selection of strategies to reduce the risk of recurrence in each patient profile.Despite the heterogeneous and retrospective nature of studies addressing this area,an international consensus is necessary to approach this clinical scenario uniformly.

PoIR: A Node Selection Mechanism in Reputation-Based Blockchain Consensus Using Bidirectional LSTM Regression Model

This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation(PoIR)as an alternative to traditional Proof of Work(PoW).PoIR addresses the limitations of existing reputationbased consensus mechanisms by proposing a more decentralized and fair node selection process.The proposed PoIR consensus combines Bidirectional Long Short-Term Memory(BiLSTM)with the Network Entity Reputation Database(NERD)to generate reputation scores for network entities and select authoritative nodes.NERD records network entity profiles based on various sources,i.e.,Warden,Blacklists,DShield,AlienVault Open Threat Exchange(OTX),and MISP(Malware Information Sharing Platform).It summarizes these profile records into a reputation score value.The PoIR consensus mechanism utilizes these reputation scores to select authoritative nodes.The evaluation demonstrates that PoIR exhibits higher centralization resistance than PoS and PoW.Authoritative nodes were selected fairly during the 1000-block proposal round,ensuring a more decentralized blockchain ecosystem.In contrast,malicious nodes successfully monopolized 58%and 32%of transaction processes in PoS and PoW,respectively,but failed to do so in PoIR.The findings also indicate that PoIR offers efficient transaction times of 12 s,outperforms reputation-based consensus such as PoW,and is comparable to reputation-based consensus such as PoS.Furthermore,the model evaluation shows that BiLSTM outperforms other Recurrent Neural Network models,i.e.,BiGRU(Bidirectional Gated Recurrent Unit),UniLSTM(Unidirectional Long Short-Term Memory),and UniGRU(Unidirectional Gated Recurrent Unit)with 0.022 Root Mean Squared Error(RMSE).This study concludes that the PoIR consensus mechanism is more resistant to centralization than PoS and PoW.Integrating BiLSTM and NERD enhances the fairness and efficiency of blockchain applications.

The Next-Hop Node Selection Based GPSR in Vehicular Ad Hoc Networks

Vehicular Ad hoc Networks (VANETs) which is a special form of Mobile Ad hoc Networks (MANETs) has promising application prospects in the future. Due to the rapid changing of topology structure, how to find a route which can guarantee Quality of Service (QoS) is an important issue in VANETs. This paper presents an improved Greedy Perimeter Stateless Routing (GPSR) protocol based on our proposed next-hop node selection mechanism. Firstly, we define the link reliability in two cases which take the movement direction angle between two vehicles into consideration. Then we propose a next-hop node selection mechanism based on a weighted function which consists of link reliability between the sender node and next-hop candidate node, distance between next-hop candidate node and the destination, movement direction angle of next-hop candidate node. At last, an improved GPSR protocol is proposed based on the next-hop node selection mechanism. Simulation results are presented to evaluate the performance of the improved GPSR protocol, which shows that the performance including packet delivery ratio and average end-to-end delay of the proposed protocol is better in some situations.

外周血E-selectin、P-selection及K-ras基因突变水平在胰腺癌中的表达及意义

目的探讨外周血内皮细胞选择素(E-selectin)、血小板选择素(P-selection)及K-ras基因突变水平在胰腺癌中的表达及意义。方法选择2019年2月—2020年2月收治的胰腺癌60例作为胰腺癌组,选择同期健康体检的健康人60例作为对照组。应用双抗夹心法酶联免疫吸附试验(ELISA)检测2组外周血E-selectin及P-selection水平,使用肽核酸钳制实时定量PCR法检测外周血中K-ras基因突变情况。分析2组外周血E-selectin、P-selection水平及其与胰腺癌临床病理特征的关系。检测外周血K-ras基因突变率及与临床病理特征的关系。结果胰腺癌组外周血E-selectin水平高于对照组,P-selection水平低于对照组(P<0.01)。外周血E-selectin、P-selection水平与肿瘤分期、淋巴结转移、肝转移有关(P<0.01),与肿瘤部位无关(P>0.05)。外周血K-ras基因突变率为48.3%。胰腺癌患者外周血K-ras基因突变与性别相关(P<0.01),而与患者年龄、分期、淋巴结及肝转移和肿瘤部位无关(P>0.05)。结论外周血E-selectin和P-selection异常表达与外周血K-ras基因检测同胰腺癌发生发展及预后密切相关,对胰腺癌早期筛查、诊断、指导临床治疗起到重要作用。

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.

Impact of antenna and beam-selection-based sectored relay planning for 備 performance evaluation of 4G LTE-A tri-sectored cell

The deployment of Relay Nodes (RNs) in 4G LTE-A networks, mainly originating from the wireless backhaul link, provides an excellent network planning tool to enhance system performance. Better coordination between the base station and relays to mitigate inter-cell interference becomes an important aspect of achieving the required system performance, not only in the single-cell scenario, but also in multi-cell scenarios. In this paper, we model and analyze two basic approaches for designing a 4G LTE-A tri-sectored cellular system. The approaches are based on Antenna Selection Sectored Relaying (ASSR) and Beam Selection Sectored Relaying (BSSR). The main purpose of the proposed schemes is to enhance system performance by improving the quality of the wireless relay backhaul link. In this technique, antenna selection takes into consideration Non-Line-Of-Sight (NLOS) communication, whereas BSSR considers the case of Line-Of-Sight (LOS) communication using heuristic beam forming approach. The resource allocation problem has also been investigated for relay based cooperative LTE-A trisectored cell in the downlink. The best possible location for relay node in the sector, power allocation and MIMO channel modeling is formulated as an optimization problem with the aim of maximizing the end to end link rate and the Signal to Interference plus Noise Ratio (SINR) of 4G LTE-A systems. Power allocation/optimization has been solved by means of the duality equation of the stationary Karush-Kuhn-Tucker (KKT) cond让ion and is used to derive optimal values for the beam forming vector on both the relay as well as the access link. The performance of the proposed scheme is verified through simulations carried out using MATLAB software. The simulation results show a significant improvement in the SINR, throughput capacity, and coverage area of the 4G LTE-A cell, while guaranteeing better quality of service.

Extracting Multiple Nodes in a Brain Region of Interest for Brain Functional Network Estimation and Classification

Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representative signals from brain regions of interest (ROIs) is important. In the past decades, the common method is generally to take a ROI as a node, averaging all the voxel time series inside it to extract a representative signal. However, one node does not represent the entire information of this ROI, and averaging method often leads to signal cancellation and information loss. Inspired by this, we propose a novel model extraction method based on an assumption that a ROI can be represented by multiple nodes. Methods: In this paper, we first extract multiple nodes (the number is user-defined) from the ROI based on two traditional methods, including principal component analysis (PCA), and K-means (Clustering according to the spatial position of voxels). Then, canonical correlation analysis (CCA) was issued to construct BFNs by maximizing the correlation between the representative signals corresponding to the nodes in any two ROIs. Finally, to further verify the effectiveness of the proposed method, the estimated BFNs are applied to identify subjects with autism spectrum disorder (ASD) and mild cognitive impairment (MCI) from health controls (HCs). Results: Experimental results on two benchmark databases demonstrate that the proposed method outperforms the baseline method in the sense of classification performance. Conclusions: We propose a novel method for obtaining nodes of ROId based on the hypothesis that a ROI can be represented by multiple nodes, that is, to extract the node signals of ROIs with K-means or PCA. Then, CCA is used to construct BFNs.

An mmWave Dual-Band Integrated Substrate Gap Waveguide Single Cavity Filter with Frequency Selectivity

A novel dual-band ISGW cavity filter with enhanced frequency selectivity is proposed in this paper by utilizing a multi-mode coupling topology.Its cavity is designed to control the number of modes,and then the ports are determined by analyzing the coupling relationship between these selected modes.By synthesizing the coupling matrix of the filter,a nonresonating node(NRN)structure is introduced to flexibly tune the frequency of modes,which gets a dualband and quad-band filtering response from a tri-band filter no the NRN.Furthermore,a frequency selective surface(FSS)has been newly designed as the upper surface of the cavity,which significantly improves the bad out-of-band suppression and frequency selectivity that often exists in most traditional cavity filter designs and measurements.The results show that its two center frequencies are f01=27.50 GHz and f02=32.92GHz,respectively.Compared with the dual-band filter that there is no the FSS metasurface,the out-of-band suppression level is improved from measured 5 dB to18 dB,and its finite transmission zero(FTZ)numbers is increased from measured 1 to 4 between the two designed bands.Compared with the tri-band and quadband filter,its passband bandwidth is expanded from measured 1.17%,1.14%,and 1.13% or 1.31%,1.50%,0.56%,and 0.57% to 1.71% and 1.87%.In addition,the filter has compact,small,and lightweight characteristics.

Machine learning-based gray-level co-occurrence matrix signature for predicting lymph node metastasis in undifferentiated-type early gastric cancer

BACKGROUND The most important consideration in determining treatment strategies for undifferentiated early gastric cancer(UEGC)is the risk of lymph node metastasis(LNM).Therefore,identifying a potential biomarker that predicts LNM is quite useful in determining treatment.AIM To develop a machine learning(ML)-based integral procedure to construct the LNM gray-level co-occurrence matrix(GLCM)prediction model.METHODS We retrospectively selected 526 cases of UEGC confirmed through pathological examination after radical gastrectomy without endoscopic treatment in four tertiary hospitals between January 2015 to December 2021.We extracted GLCM-based features from grayscale images and applied ML to the classification of candidate predictive variables.The robustness and clinical utility of each model were evaluated based on the following factors:Receiver operating characteristic curve(ROC),decision curve analysis,and clinical impact curve.RESULTS GLCM-based feature extraction significantly correlated with LNM.The top 7 GLCM-based factors included inertia value 0°(IV_0),inertia value 45°(IV_45),inverse gap 0°(IG_0),inverse gap 45°(IG_45),inverse gap full angle(IG_all),Haralick 30°(Haralick_30),Haralick full angle(Haralick_all),and Entropy.The areas under the ROC curve(AUCs)of the random forest classifier(RFC)model,support vector machine,eXtreme gradient boosting,artificial neural network,and decision tree ranged from 0.805[95%confidence interval(CI):0.258-1.352]to 0.925(95%CI:0.378-1.472)in the training set and from 0.794(95%CI:0.237-1.351)to 0.912(95%CI:0.355-1.469)in the testing set,respectively.The RFC(training set:AUC:0.925,95%CI:0.378-1.472;testing set:AUC:0.912,95%CI:0.355-1.469)model that incorporates Entropy,Haralick_all,Haralick_30,IG_all,IG_45,IG_0,and IV_45 had the highest predictive accuracy.CONCLUSION The evaluation results indicate that the method of selecting radiological and textural features becomes more effective in the LNM discrimination against UEGC patients.Additionally,the MLbased prediction model developed using the RFC can be used to derive treatment options and identify LNM,which can hence improve clinical outcomes.

Research on reference nodes placement and selection of ubiquitous computing locating service

Obtaining the location of an unknown node accurately is a key problem of a locating service under a ubiquitous computing environment.The paper proposes and proves three theorems of location reference node placement according to the analysis of the location error produced during location using a polygon location method and three important characteristics of chaos dynamics.Based on the three theorems,the location reference node selection(LRNS)algorithm is proposed by improving on the traditional polygon location algorithm.The simulation results indicate that the reference node placement theorems and the LRNS algo-rithm can meet the requirements of a ubiquitous terminal’s real-time location and possess a preferable precision in location.

无线传感网络拥塞控制中节点选择强制博弈方法

无线传感网络中节点数据流量较大,分布范围较广,节点选择寻优计算复杂,导致浪费大量算力在节点选择上,拥塞控制效果不佳。提出一种无线传感网络拥塞控制中节点选择强制博弈方法,计算单条网络链路传输成本,依据传输数据耗费能量,计算传输节点可用能量比值,在传输变量权重值基础上,求得传输节点实际拥堵指数,完成节点选择。计算网络连接层的数据包丢弃概率,确定网络拥塞程度,汇聚节点数据流建立节点选择强制博弈模型,明确传感网络稳态传输条件,利用流量正态分布算法,在节点选择博弈中做出强制选择。经仿真分析证明:所提方法分组递交率保持在83%以上,丢包数量保持在100 pkt/s之内,网络传输节点端到端的延迟在0.3 s以内。

面向O-RAN的多级异构边缘资源分配与部署

为应对开放型无线接入网(Open Radio Access Network,O-RAN)中的数据传输成本过高及网络兼容不足等问题,研究了面向O-RAN的多级边缘服务资源分配与部署联合优化问题。首先,利用四层融合模型将多目标联合优化问题转化为异构边缘服务器数量选择及位置确定问题,并提出了一种负载约束和迭代优化的异构边缘服务器资源分配算法,解决了O-RAN网络中的异构资源分配与数据传输问题。然后,提出了一种能效驱动的异构节点部署优化算法,解决了多级异构资源最佳部署位置问题。最后,利用上海电信基站的真实数据集,验证了所提资源优化与部署算法的有效性,实验结果表明,所提算法较其它算法在部署成本上至少降低了22.5%,能效比值上至少提高了25.96%。

考虑节点重要度的城市轨道交通应急救援点选址研究

为实现对城市轨道交通中关键站点的识别和保护,提出1种考虑节点重要度的应急救援点选址方法,基于复杂网络理论构建城市轨道交通网络,从节点拓扑属性、车站属性、网络连通属性3方面出发建立节点重要度评价方法,构建节点重要度加权下的综合覆盖水平最大、最大救援响应时间最短和建设成本最低的多目标应急救援点选址模型,并以武汉地铁网络为例,基于熵权-TOPSIS法确定网络中共262个节点的重要度,设计变邻域遗传算法进行优化求解。研究结果表明:4种赋权方案下发现,相对不考虑节点重要度,本文方法所得目标在综合覆盖水平上分别提高1.44%,1.06%,2.51%和2.90%,同时可有效降低最大救援响应时间及建设成本,识别与保护关键站点。研究结果可为提升线网整体的应急能力提供指导。

基于互联互通的市域铁路网络节点模式选择

研究目的:采用系统性方法探讨互联互通条件下市域铁路网络节点模式的选择方法,以上海市域铁路网络为例,旨在实现市域铁路网络的高效运行和区域的有机连接。本文研究集中于互联互通节点的功能定位、枢纽等级、位置选择及其与近沪城市的有效衔接,以期提供一种优化市域铁路网络规划和建设的科学方法。研究结论:(1)互联互通节点的合理选择和布局对于提升市域铁路网络的运行效率和区域连接性具有决定性意义;(2)互联互通节点的功能定位、枢纽等级和位置选择是实现市域铁路网络高效运行的关键因素;(3)通过系统性研究和分析上海市域铁路网络,提出的节点模式选择方法可为其他城市的市域铁路规划和建设提供参考。

结合节点特征和非合作博弈的选择性转发攻击检测

针对无线传感器网络的选择性转发攻击行为,提出了一种结合节点特征和非合作博弈的选择性转发攻击检测(node characteristics and non-cooperative game for selective forwarding attack detection,NC-NCG)方法。该方法通过设置独立监督网络环境,将节点特征中的转发率与门限阈值进行比较,计算小于阈值节点的当前转发率与T时间内平均转发率的偏离程度,根据偏离程度进行二次判定,以提高选择性转发攻击的检测率。同时为提高网络吞吐量,构建了不完全信息的非合作博弈模型,迫使可疑节点参与网络功能,实现节点快速识别。仿真实验结果表明,该方法不仅能够有效识别选择性转发攻击,而且在资源有限的情况下,可以提高网络吞吐量并延长网络生命周期。