Clustered张拉整体结构的动力学建模

针对clustered张拉整体结构,提出了一种基于任意拉格朗日-欧拉(arbitrary Lagrangian-Eulerian, ALE)法的多体动力学模型。相较于传统的拉格朗日法模型,该研究中的模型具有更为简单的运动学约束。首先,引入了一种ALE时变长度索单元,其网格节点与物质点可以独立运动,提供了一种自然的方式描述结构中运动的滑轮和滑动绳索;其次,利用达朗贝尔原理,推导了该单元的广义力向量并计算了相应的雅可比矩阵;然后,建立了张拉整体系统的动力学方程,并利用广义α算法对其进行求解,选取节点的全局位置坐标和物质坐标作为广义坐标,其中全局位置坐标可以被不同物体共享,以减少动力学方程的自由度数和消除物体间的约束;最后,展示了一个数值算例,10层可折叠张拉整体塔架,对其折叠过程进行了准静态和动力学仿真,验证了模型的有效性。所提出的模型和算法可为clustered张拉整体结构的设计提供理论指导,具有工程意义。

A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

Simultaneous Partitioning and Scheduling Algorithm for Clustered Architecture

Clustered architecture is selected for high level synthesis,and a simultaneous partitioning and scheduling algorithm are proposed.Compared with traditional methods,circuit performance can be improved.Experiments show the efficiency of the method.

Efficient Subchannel Allocation Based on Clustered Interference Alignment in Ultra-Dense Femtocell Networks

In this paper, we exploit clustered interference alignment(IA) for efficient subchannel allocation in ultra-dense orthogonal frequency division multiplexing access(OFDMA) based femtocell networks, which notably improves the spectral efficiency as well as addresses the feasibility issue of IA. Our problem is formulated as a combinatorial optimization problem which is NP-hard. To avoid obtaining its optimal solution by exhaustive search, we propose a two-phases efficient solution with low-complexity. The first phase groups all the femtocell user equipments(FUEs) into disjoint clusters, and the second phase allocates subchannels to the formed clusters where IA is performed. By doing this, the intra-cluster and inter-cluster interferences are mitigated by clustered IA and subchannel allocation in ultra-dense femtocell networks, respectively.Also, low-complexity algorithm is proposed to solve the corresponding sub-problem in each phase. Simulation results demonstrate that the proposed scheme not only outperforms other related schemes, but also provides a close performance to the optimal solution.

Evolutionary prisoner's dilemma game on highly clustered community networks

This paper studies the evolutionary prisoner＇s dilemma game on a highly clustered community network in which the clustering coefficient and the community size can be tuned. It finds that the clustering coefficient in such a degree-homogeneous network inhibits the emergence of cooperation for the entire range of the payoff parameter. Moreover, it finds that the community size can also have a marked influence on the evolution of cooperation, with a larger community size leading to not only a lower cooperation level but also a smaller threshold of the payoff parameter above which cooperators become extinct.

Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks

The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,etc.Besides,the industrial Internet of things(IIoT)directs data from systems for monitoring and controlling the physical world to the data processing system.A major novelty of the IIoT is the unmanned aerial vehicles(UAVs),which are treated as an efficient remote sensing technique to gather data from large regions.UAVs are commonly employed in the industrial sector to solve several issues and help decision making.But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs.Federated learning(FL)becomes a recent advancement of machine learning(ML)which aims to protect user data.In this aspect,this study designs federated learning with blockchain assisted image classification model for clustered UAV networks(FLBIC-CUAV)on IIoT environment.The proposed FLBIC-CUAV technique involves three major processes namely clustering,blockchain enabled secure communication and FL based image classification.For UAV cluster construction process,beetle swarm optimization(BSO)algorithm with three input parameters is designed to cluster the UAVs for effective communication.In addition,blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers.Finally,the cloud server uses an FL with Residual Network model to carry out the image classification process.A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach.The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.

Classification of clustered microseismic events in a coal mine using machine learning

Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated.In this research,two groups of seismic events with a minimum local magnitude(ML) of-3 were observed in an underground coal mine.They were respectively located around a dyke and the longwall face.Additionally,two types of undesired signals were also recorded.Four machine learning methods,i.e.random forest(RF),support vector machine(SVM),deep convolutional neural network(DCNN),and residual neural network(ResNN),were used for classifying these signals.The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy.The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy.As mining is a dynamic progress which could change the characteristics of seismic signals,the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining.A cascaded workflow consisting of database update,model training,signal prediction,and results review was established.By progressively calibrating the DCNN model,it achieved up to 99% prediction accuracy.The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.

Energy Aware Clustered Based Multipath Routing in Mobile Ad Hoc Networks

With the advance of wireless communication technologies, small-size and high-performance computing and communication devices are increasingly used in daily life. After the success of second generation mobile system, more interest was started in wireless communications. A Mobile Ad hoc Network (MANET) is a wireless network without any fixed infrastructure or centralized control;it contains mobile nodes that are connected dynamically in an arbitrary manner. The Mobile Ad hoc Networks are essentially suitable when infrastructure is not present or difficult or costly to setup or when network setup is to be done quickly within a short period, they are very attractive for tactical communication in the military and rescue missions. They are also expected to play an important role in the civilian for as convention centers, conferences, and elec-tronic classrooms. The clustering is an important research area in mobile ad hoc networks because it im-proves the performance of flexibility and scalability when network size is huge with high mobility. All mo-bile nodes operate on battery power;hence, the power consumption becomes an important issue in Mobile Ad hoc Network. In this article we proposed an Energy Aware Clustered-Based Multipath Routing (EACMR), which forms several clusters, finds energy aware node-disjoint multiple routes from a source to destination and increases the network life time by using optimal routes.

Co-word clustering analysis for nursing safety management research focuses by PubMed

Objective： To analyze hot research areas and the present research status of nursing safety management in PubMed. Methods： PubMed was searched using ＂safety management＂ for the literature on nursing safety management. BICOMB 2.0 and SPSS 20.0 software were used to analyze high-frequency keywords and conduct co-word clustering analysis. Results： We searched for totally 2353 articles related to our topic and extracted 19 high-frequency keywords （27.50%）. Five research focuses were concluded, including： study on nursing safety culture; team work to promote nursing safety; practice of nursing safety management; workplace violence against nursing staffs; nursing safety and quality evaluation standard. Conclusion： Analysis of the hotspots of nursing safety management in the past 10 years will contribute to understanding the research emphases and trend of development, and provide reference for the study and practice of nursing safety management.

A Sensor Anonymity Enhancement Scheme Based on Pseudonym for Clustered Wireless Sensor Network

Security problem is an important issue for Wireless Sensor Network.The paper focuses on the privacy protection of WSN applications.An anonymity enhancement tactic based on pseudonym mechanism is presented for clustered Wireless Sensor Network,which provides anonymity for both the sensors within a cluster and the cluster head nodes.Simulation experiments are launched through NS2 platform to validate the anonymity performance.The theoretical analysis and empirical study imply that the proposed scheme based on pseudonym can protect the privacies of both the sensor nodes and the cluster head nodes.The work is valuable and the experimental results are convincible.

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking applications.Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks.In this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted WSN.The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and lifetime.The proposed model involves two stage processes namely clustering and routing.Firstly,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct clusters.Besides,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted WSN.The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,etc.The proposed EMOQoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH,PSO-ECHS,E-OEERP,and iCSHS methods have resulted in a lesser NAN of 2,10,42,and 51 rounds.The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

Kangaroo Care (Skin-to-Skin) for Clustered Pain Procedures: Case Study

Background: Pain management for term newborns undergoing clustered painful procedures has not been tested. Kangaroo Care (chest-to-chest, skin-to-skin position of infant on mother) effectively reduces pain of single procedures, but its effect on pain from clustered procedures is not known. Aim: The aim was to test Kangaroo Care’s effect on pain in one term infant who received clustered painful procedures while determining feasibility of the Kangaroo Care intervention. Design, Setting, and Participant: A case study design was used with one healthy term newborn who received two heel sticks and one injection in one session in the mother’s postpartum room. Method: Heart rate and oxygen saturation (recorded from Massimo Pulse Oximeter every 30 seconds), crying time (total seconds of crying on videotape) and behavioral state (using Anderson Behavioral State Scoring system every 30 seconds) were measured before (5 minutes), during (10.5 minutes) and after (30 minutes) the three clustered painful procedures in a newborn who was in Kangaroo Care during all observations. One staff nurse administered the clustered procedures. Results: Heart rate increased sequentially with each heelstick, oxygen saturation remained unchanged, sleep predominated, and crying was minimal throughout the procedures. Conclusion: Kangaroo Care appeared to reduce pain from clustered painful procedures and can be further tested.

A Comparative Study of Spatially Clustered Distribution of Jumbo Flying Squid(Dosidicus gigas)Offshore Peru

We examined spatially clustered distribution of jumbo flying squid(Dosidicus gigas) in the offshore waters of Peru bounded by 78?–86?W and 8?–20?S under 0.5?×0.5? fishing grid. The study is based on the catch-per-unit-effort(CPUE) and fishing effort from Chinese mainland squid jigging fleet in 2003–2004 and 2006–2013. The data for all years as well as the eight years(excluding El Ni?o events) were studied to examine the effect of climate variation on the spatial distribution of D. gigas. Five spatial clusters reflecting the spatial distribution were computed using K-means and Getis-Ord Gi* for a detailed comparative study. Our results showed that clusters identified by the two methods were quite different in terms of their spatial patterns, and K-means was not as accurate as Getis-Ord Gi*, as inferred from the agreement degree and receiver operating characteristic. There were more areas of hot and cold spots in years without the impact of El Ni?o, suggesting that such large-scale climate variations could reduce the clustering level of D. gigas. The catches also showed that warm El Ni?o conditions and high water temperature were less favorable for D. gigas offshore Peru. The results suggested that the use of K-means is preferable if the aim is to discover the spatial distribution of each sub-region(cluster) of the study area, while Getis-Ord Gi* is preferable if the aim is to identify statistically significant hot spots that may indicate the central fishing ground.

A clustered routing protocol based on energy and link quality in WSNs

Influenced by the environment and nodes status,the quality of link is not always stable in actual wireless sensor networks( WSNs). Poor links result in retransmissions and more energy consumption. So link quality is an important issue in the design of routing protocol which is not considered in most traditional clustered routing protocols. A based on energy and link quality's routing protocol( EQRP) is proposed to optimize the clustering mechanism which takes into account energy balance and link quality factors. EQRP takes the advantage of high quality links to increase success rate of single communication and reduce the cost of communication. Simulation shows that,compared with traditional clustered protocol,EQRP can perform 40% better,in terms of life cycle of the whole network.

基于Blending-Clustering集成学习的大坝变形预测模型

【目的】变形是反映大坝结构性态最直观的效应量,构建科学合理的变形预测模型是保障大坝安全健康运行的重要手段。针对传统大坝变形预测模型预测精度低、误报率高等问题导致的错误报警现象,【方法】选取不同预测模型和聚类算法集成,构建了一种Blending-Clustering集成学习的大坝变形预测模型,该模型以Blending对单一预测模型集成提升预测精度为核心,并通过Clustering聚类优选预测值改善模型稳定性。以新疆某面板堆石坝变形监测数据为实例分析,通过多模型预测性能比较,对所提出模型的预测精度和稳定性进行全面评估。【结果】结果显示:Blending-Clustering模型将预测模型和聚类算法集成,均方根误差(RMSE)和归一化平均百分比误差(nMAPE)明显降低,模型的预测精度得到显著提高;回归相关系数(R~2)得到提升,模型具备更强的拟合能力;在面板堆石坝上22个测点变形数据集上的预测评价指标波动范围更小,模型的泛化性和稳定性得到有效增强。【结论】结果表明:Blending-Clustering集成预测模型对于预测精度、泛化性和稳定性均有明显提升,在实际工程具有一定的应用价值。

Cluster Head Selection Algorithm for UAV Assisted Clustered IoT Network Utilizing Blockchain

To guarantee the security of Internet of Things(IoT)devices,the blockchain tech⁃nology is often applied to clustered IoT networks.However,cluster heads(CHs)need to un⁃dertake additional control tasks.For battery-powered IoT devices,the conventional CH se⁃lection algorithm is limited.Based on the above problem,an unmanned aerial vehicle(UAV)network assisted clustered IoT system is proposed,and a corresponding UAV CH se⁃lection algorithm is designed.In this scheme,UAVs are selected as CHs to serve IoT clus⁃ters.The proposed CH selection algorithm considers the maximal transmit power,residual energy and distance information of UAVs,which can greatly extend the working life of IoT clusters.Through Monte Carlo simulation,the key performance indexes of the system,in⁃cluding energy consumption,average secrecy rate and the maximal number of data packets received by the base station(BS),are evaluated.The simulation results show that the pro⁃posed algorithm has great advantages compared with the existing CH selection algorithms.

Research status and hotspots of economic evaluation in nursing by co-word clustering analysis

Objective:The aim of this study is to discover research status and hotspots of economic evaluation(EE)in nursing area using co-word cluster analysis.Methods:Medical Subject Heading(MeSH)term“cost–benefit analysis”was searched in PubMed and nursing journals were limited by the function of filter.The information of author,country,year,journal,and keywords of collected paper was extracted and exported to Bicomb 2.0 system,where high-frequency terms and other data could be further mined.SPSS 19.0 was used for cluster analysis to generate dendrogram.Results:In all,3,020 articles were found and 10,573 MeSH terms were detected;among them,1,909 were MeSH major topics and generated 42 high-frequency terms.The consequence of dendrogram showed seven clusters,representing seven research hotspots:skin administration,infection prevention,education program,nurse education and management,EE research,neoplasm patient,and extension of nurse function.Conclusions:Nursing EE research involved multiple aspects in nursing area,which is an important indicator for decision-making.Although the number of papers is increasing,the quality of study is not promising.Therefore,further study may be required to detect nurses’knowledge of economic analysis method and their attitude to apply it into nursing research.More nursing economics course could carry out in nursing school or hospitals.

Clustered Regularly Interspaced Short Palindromic Repeats(CRISPR):A critical overview on the most promising applications of molecular scissors in oral medicine

The scientific community is continuously working to translate the novel biomedical techniques into effective medical treatments.CRISPR-Cas9 system(Clustered Regularly Interspaced Short Palindromic Repeats-9),commonly known as the“molecular scissor”,represents a recently developed biotechnology able to improve the quality and the efficacy of traditional treatments,related to several human diseases,such as chronic diseases,neurodegenerative pathologies and,interestingly,oral diseases.Of course,dental medicine has notably increased the use of biotechnologies to ensure modern and conservative approaches:in this landscape,the use of CRISPR-Cas9 system may speed and personalize the traditional therapies,ensuring a good predictability of clinical results.The aim of this critical overview is to provide evidence on CRISPR efficacy,taking into specific account its applications in oral medicine.

Nx-SCFDMA和clustered DFT-S-OFDMA方案研究

随着宽带无线通信的发展,目前的多址技术已经满足不了系统的需求,为了适应更宽的带宽的传输,必须引进增强型的多址接入技术。介绍了4G系统讨论的其中两种热点多址方案,即:Nx-SCFDMA(Nx单载波频分多址接入)、clustered DFT-S-OFDMA(clustered离散傅里叶变换扩展频分多址接入),主要从方案的实现原理、CM(立方量度)性能和FFT(快速傅里叶变换)复杂度方面进行分析和比较。