Number Of Cluster Members Research Articles

Clustering Properties of Intermediate and High-mass Young Stellar Objects* * Table is also available online at the CDS in the VizieR archive service.

We have selected 337 intermediate- and high-mass young stellar objects (YSOs; 1.5–20 M ⊙) well-characterized with spectroscopy. By means of the clustering algorithm HDBSCAN, we study their clustering and association properties in the Gaia DR3 catalog as a function of stellar mass. We find that the lower-mass YSOs (1.5–4 M ⊙) have clustering rates of 55%–60% in Gaia astrometric space, a percentage similar to that found in the T Tauri regime. However, intermediate-mass YSOs in the range 4–10 M ⊙ show a decreasing clustering rate with stellar mass, down to 27%. We find tentative evidence suggesting that massive YSOs (>10 M ⊙) often (yet not always) appear clustered. We put forward the idea that most massive YSOs form via a mechanism that demands many low-mass stars around them. However, intermediate-mass YSOs form in a classical core-collapse T Tauri way, yet they do not appear often in the clusters around massive YSOs. We also find that intermediate- and high-mass YSOs become less clustered with decreasing disk emission and accretion rate. This points toward an evolution with time. For those sources that appear clustered, no major correlation is found between their stellar properties and the cluster sizes, number of cluster members, cluster densities, or distance to cluster centers. In doing this analysis, we report the identification of 55 new clusters. We tabulated all of the derived cluster parameters for the considered intermediate- and high-mass YSOs.

The multiplicity fraction in 202 open clusters from Gaia

In this study, we estimate the fraction of binaries with high mass ratios for 202 open clusters in the extended solar neighbourhood (closer than 1.5 kpc from the Sun). This is one of the largest homogeneous catalogues of multiplicity fractions in open clusters to date, including the unresolved and total (close-binary) multiplicity fractions of main-sequence systems with mass ratio greater than $ 0.6_{ -0.15}^{+0.05} $. The unresolved multiplicity fractions were estimated applying a flexible mixture model to the observed Gaia colour-magnitude diagrams of the open clusters. Then we used custom Gaia simulations to account for the resolved systems and derived the total multiplicity fractions. The studied open clusters have ages between 6.6 Myr and 3.0 Gyr and total high-mass-ratio multiplicity fractions between 6% and 80%, with a median of 18%. The multiplicity fractions increase with the mass of the primary star, as expected. The average multiplicity fraction per cluster displays an overall decreasing trend with the open cluster age up to ages about 100 Myr, above which the trend increases. Our simulations show that most of this trend is caused by complex selection effects (introduced by the mass dependence of the multiplicity fraction and the magnitude limit of our sample). Furthermore, the multiplicity fraction is not significantly correlated with the clusters’ position in the Galaxy. The spread in multiplicity fraction decreases significantly with the number of cluster members (used as a proxy for cluster mass). We also find that the multiplicity fraction decreases with metallicity, in line with recent studies using field stars.

A detection of the layered structure of nearby open clusters

We applied the newly developed rose diagram overlay method to detect the layered structure of 88 nearby open clusters (≤500 pc) on the three projections after the distance correction of their member stars, based on the catalog in literature. The results show that with the rose diagram overlay method, a total of 74 clusters in our sample have a layered structure, while the remaining clusters are without a clear layered structure. We for the first time defined the layered structure parameters for the sample clusters. Meanwhile, we found that the layered circle core area (s) has a strong positive correlation with the number of cluster members, while the kernel instability index (η) has a strong negative correlation with the number of cluster members. Our study provides a novel perspective for the detection of the layered structure of open clusters.

The Gaia-ESO survey: A spectroscopic study of the young open cluster NGC 3293

We present a spectroscopic analysis of the GIRAFFE and UVES data collected by the Gaia-ESO survey for the young open cluster NGC 3293. Archive spectra from the same instruments obtained in the framework of the ‘VLT-FLAMES survey of massive stars’ are also analysed. Atmospheric parameters, non-local thermodynamic equilibrium (LTE) chemical abundances for six elements (He, C, N, Ne, Mg, and Si), or variability information are reported for a total of about 160 B stars spanning a wide range in terms of spectral types (B1 to B9.5) and rotation rate (up to 350 km s−1). Our analysis leads to about a five-fold increase in the number of cluster members with an abundance determination and it characterises the late B-star population in detail for the first time. We take advantage of the multi-epoch observations on various timescales and a temporal baseline, sometimes spanning ∼15 years, to detect several binary systems or intrinsically line-profile variables. A deconvolution algorithm is used to infer the current, true (deprojected) rotational velocity distribution. We find a broad, Gaussian-like distribution peaking around 200–250 km s−1. Although some stars populate the high-velocity tail, most stars in the cluster appear to rotate far from critical. We discuss the chemical properties of the cluster, including the low occurrence of abundance peculiarities in the late B stars and the paucity of objects showing CN-cycle burning products at their surface. We argue that the former result can largely be explained by the inhibition of diffusion effects because of fast rotation, while the latter is generally in accord with the predictions of single-star evolutionary models under the assumption of a wide range of initial spin rates at the onset of main-sequence evolution. However, we find some evidence for a less efficient mixing in two quite rapidly rotating stars that are among the most massive objects in our sample. Finally, we obtain a cluster age of ∼20 Myr through a detailed, star-to-star correction of our results for the effect of stellar rotation (e.g., gravity darkening). This is significantly older than previous estimates from turn-off fitting that fully relied on classical, non-rotating isochrones.

Spatial concentration of creative industries and location of creative clusters in Poland

The authors compared the geographical distribution of creative clusters in Poland with the spatial distribution of LQ (Location Quotient) measuring the concentration of creative entities in a given area. The study aimed to answer the following questions: what is the spatial distribution of clustering potential for creative industries?, which municipalities are home to entities forming creative clusters?, and, above all, are creative clusters actually formed in areas offering the greatest potential in this respect? In order to find answers to the above questions, the authors carried out two sub-analyses. The first led to the conclusion that only 1.9% of municipalities in Poland offer conditions for creative specialisations, including the formation of creative clusters (LQ > 1.25). The next step involved a comparison of geographical distribution of areas with clustering potential with areas being home to actual creative clusters. The authors did not observe any relationship between the value of LQ reflecting clustering potential for creative industries and the distribution and size of active creative clusters. The correlation coefficient between LQ and the number of cluster members was positive but weak. It shows that creative clusters in Poland are not formed in locations offering best conditions in terms of the number of potential members. Therefore, there are other factors behind the formation of cluster structures. Nevertheless, local potential should be the main driving force because it is one of the crucial components of favourable conditions for building economic specialisation and a foundation on which proper cluster structures should be built on.

Implementasi Algoritma Clustering Partitioning Around Medoid (PAM) dalam Clustering Virus MERS-Cov

<p><em>Th</em><em>e Middle East Respiratory Coronavirus (MERS-CoV) is a disease caused by a coronavirus. This virus is contagious, but its transmission is not as easy as the common cold, MERS-CoV virus is better susceptible to transmitting through direct contact, for example in people who care about the MERS-CoV virus without the need for virus protection. To determine the characteristics, the MERS-CoV disease virus can be identified by identifying DNA (deoxyribonucleic acid). One technique in understanding the characteristics of life is by grouping. Grouping can be done by grouping DNA into groups that have attributes and functions. The Clustering algorithm used in this study is Partitioning Around Medoid (PAM). This algorithm has the advantage that the results of the grouping process are not by following the order of entering the dataset and overcome sensitivity to noise and outliers. The purpose of this study is to implement the Partitioning Around Medoid (PAM) clustering algorithm in clustering the MERS-CoV virus. This research was conducted through a quantitative descriptive literature study. The implementation of the PAM algorithm on the MERS-CoV DNA sequence obtained 2 clusters with the highest silhouette coefficient value on the number of clusters 2, namely 0.61534. The number of members in Cluster 1 is 84 MERS-CoV DNA sequences and the number of members in Cluster 2 is 16 MERS-CoV DNA sequences.</em></p>

APTEEN routing protocol optimization in wireless sensor networks based on combination of genetic algorithms and fruit fly optimization algorithm

APTEEN routing protocol exists the problems of uneven network energy consumption, premature death of some nodes, consume too much unnecessary energy and low effective coverage of the whole network. To solve these problems, this paper optimizes the APTEEN routing protocol by combining genetic algorithm with fruit fly optimization algorithm. By adding residual energy, distance from node to base station, distance from node to geometric center of the whole network, node degree and other selection factors to cluster heads selection, the genetic algorithm and fruit fly optimization algorithm is used to select cluster heads for the first time, and the second time of cluster heads selection based on density adaptive algorithm. Some nodes are selected to sleep according to the position and degree of nodes. The residual energy of cluster head, the distance between node and cluster head, and the number of cluster members are taken into account when nodes join clusters. When energy is transmitted from cluster heads to base station, the Dijkstra algorithm is used to find the optimal path. Add the rule of rotating cluster heads when the energy consumption of data transmission is too high, and the GA-APTEEN routing protocol is obtained through the above optimization. The simulation results show that the GA-APTEEN improves the 50% lifetime, 10% coverage and robustness of the network, reduces the energy consumption of the overall network system and avoids the phenomenon of the hot zone of energy.

A Bio-Inspired Solution to Cluster-Based Distributed Spectrum Allocation in High-Density Cognitive Internet of Things

With the emergence of Internet of Things (IoT), where any device is able to connect to the Internet and monitor/control physical elements, several applications were made possible, such as smart cities, smart health care, and smart transportation. The wide range of the requirements of these applications drives traditional IoT to cognitive IoT (CIoT) that supports smart resource allocation, automatic network operation and intelligent service provisioning. To enable CIoT, there is a need for flexible and reliable wireless communication. In this paper, we propose to combine cognitive radio (CR) with a biological mechanism called reaction–diffusion to provide efficient spectrum allocation for CIoT. We first formulate the quantization of qualitative connectivity-flexibility tradeoff problem to determine the optimal cluster size (i.e., number of cluster members) that maximizes clustered throughput but minimizes communication delay. Then, we propose a bio-inspired algorithm which is used by CIoT devices to form cluster distributedly. We compute the optimal values of the algorithm’s parameters (e.g., contention window) of the proposed algorithm to increase the network’s adaption to different scenarios (e.g., spectrum homogeneity and heterogeneity) and to decrease convergence time, communication overhead, and computation complexity. We conduct a theoretical analysis to validate the correctness and effectiveness of proposed bio-inspired algorithm. Simulation results show that the proposed algorithm can achieve excellent clustering performance in different scenarios.

A QoS-Aware Data Collection Protocol for LLNs in Fog-Enabled Internet of Things

Improving quality of service (QoS) of low power and lossy networks (LLNs) in Internet of things (IoT) is a major challenge. Cluster-based routing technique is an effective approach to achieve this goal. This paper proposes a QoS-aware clustering-based routing (QACR) mechanism for LLNs in Fog-enabled IoT which provides a clustering, a cluster head (CH) election, and a routing path selection technique. The clustering adopts the community detection algorithm that partitions the network into clusters with available nodes' connectivity. The CH election and relay node selection both are weighted by the rank of the nodes which take node's energy, received signal strength, link quality, and number of cluster members into consideration as the ranking metrics. The number of CHs in a cluster is adaptive and varied according to a cluster state to balance the energy consumption of nodes. Besides, the protocol uses the CH role handover technique during CH election that decreases the control messages for the periodic election and cluster formation in detail. An evaluation of the QACR has performed through simulations for various scenarios. The obtained results show that the QACR improves the QoS in terms of packet delivery ratio, latency, and network lifetime compared to the existing protocols.

A cluster‐based fault‐tolerant routing protocol for wireless sensor networks

SummaryEnergy conservation and fault tolerance are two critical issues in the deployment of wireless sensor networks (WSNs). Many cluster‐based fault‐tolerant routing protocols have been proposed for energy conservation and network lifetime maximization in WSNs. However, these protocols suffer from high frequency of re‐clustering as well as extra energy consumption to tolerate failures and consider only some very normal parameters to form clusters without any verification of the energy sufficiency for data routing. Therefore, this paper proposes a cluster‐based fault‐tolerant routing protocol referred as CFTR. This protocol allows higher energy nodes to become Cluster Heads (CHs) and operate multiple rounds to diminish the frequency of re‐clustering. Additionally, for the sake to get better energy efficiency and balancing, we introduce a cost function that considers during cluster formation energy cost from sensor node to CH, energy cost from CH to sink, and another significant parameter, namely, number of cluster members in previous round. Further, the proposed CFTR takes care of nodes, which have no CH in their communication range. Also, it introduces a routing algorithm in which the decision of next hop CH selection is based on a cost function conceived to select routes with sufficient energy for data transfer and distribute uniformly the overall data‐relaying load among the CHs. As well, a low‐overhead algorithm to tolerate the sudden failure of CHs is proposed. We perform extensive simulations on CFTR and compare their results with those of two recent existing protocols to demonstrate its superiority in terms of different metrics.

Development of Clusters in Poland

The current study results attribute great importance to the role of clusters in innovation and competitive development creation. While seeking not so much the operational solutions, but the main components that form the cluster management process, it was noticed that a cluster – despite being already well- defined— does not come into being together with the declaration, willingness, initiative or the signing of the document. The creation and development of clusters is a long term process that can last for years and is exposed to a number of strategic obstacles. The experience both on a national and global level relating to the dynamics of the development of clusters show how difficult this task is, especially in a knowledge-based economy. The development of specialization and knowledge in clusters is no longer linear in nature, based on knowledge generated by the function of research and development of an individual company. It is defined as the result of a process of interaction through inter-organizational relationships, providing access to various types of resources. The article assumes that clusters in Poland after the quantum boom, are moving to the next phase of development based on improvement of activities. The aim of the article is to present the dynamics of cluster development in Poland. The research was done in 2015-2017, but the research period covered the years 2003-2016. Besides basic information on: number of clusters, year of creation, number of cluster members etc., the article analyzes cluster specializations against the background of smart specializations being developed in regions in Poland. Furthermore, the article presents the preliminary results of research on meeting management standards by Polish clusters. The research was based on PARP (The Polish Agency for Enterprise Development) project data and own studies.

Low Energy Fuzzy based Unequal Clustering Multihop Architecture for Wireless Sensor Networks

This paper presents a Low Energy Fuzzy based Unequal Clustering Multihop Architecture (LEFUCMA) for wireless sensor networks consisting of several nodes that send sensed data to a Master Station (MS). LEFUCMA encompasses neighbor finding, cluster head selection, clustering and routing protocols. The neighbor finding protocol organizes the network into a sectored-layers structure. The cluster head selection uses fuzzy logic with residual energy, number of neighboring nodes, packet reception rate and distance of node from MS as fuzzy descriptors for cluster head selection. For even distribution of traffic, LEFUCMA uses fuzzy logic with node density and distance of area from MS as fuzzy descriptors to decide number of cluster heads in a given area. To avoid hot spots problem, LEFUCMA uses an unequal clustering mechanism with clusters closer to MS having smaller sizes than those farther from MS. Finally, inter-cluster routing protocol decides the next hop cluster head considering its residual energy, distance from MS and from current cluster head which represents energy required for communication, number of cluster members which represents intra-cluster traffic and number of descendant nodes which represents inter-cluster traffic. A comparative analysis of LEFUCMA; Unequal Hybrid, Energy Efficient and Distributed Clustering (Ever et al. in Proceedings of international conference on sensor networks, pp 185–193, 2012; Energy Aware Distributed Unequal Clustering (Yu et al. in Hindawi Int J Distrib Sens Netw, Article ID 202145:1–8, 2011); Constructing Optimal Clustering Architecture (Li et al. in J Comput Commun 36(3):256–268, 2013; and Energy Aware Unequal Clustering using Fuzzy logic (EAUCF) (Bagci and Yazici in J Appl Soft Comput 13(4):1741–1749, 2013) shows that LEFUCMA is 32–42% more energy efficient compared to EAUCF. Throughput of LEFUCMA is 46% more and network lifetime is 60–75% more compared to EAUCF.

An Unequal Cluster-based Routing Protocol Based on Data Controlling for Wireless Sensor Network

<p>Wireless Sensor Networks (WSN) differ from traditional wireless communication networks in several characteristics. One of these characteristics is power awarness, due to the fact that the batteries of sensor nodes have a restricted lifetime and are difficult to be replaced. Therefore, all protocols must be designed to minimize energy consumption and preserve the longevity of the network. In this paper, we propose (i) to fairly balance the load among nodes. For this, we generate an unequal clusters size where the cluster heads (CH) election is based on energy availability, (ii) to reduce the energy consumption due to the transmission by using multiple metrics in the CH jointure process and taking into account the link cost, residual energy and number of cluster members to construct the routing tree and (iii) to minimize the number of transmissions by avoiding the unnecessary updates using sensitive data controller. Simulation results show that our Advanced Energy-Efficient Unequal Clustering (AEEUC) mechanism improves the fairness energy consumption among all sensor nodes and achieves an obvious improvement on the network lifetime.</p>

A hierarchical packet forwarding mechanism for energy harvesting wireless sensor networks

Energy harvesting technologies have gained widespread attention for their perpetual energy supply for sensor nodes. However, the energy resources are still insufficient while the harvesting module is added on the node. To prolong the network lifetime and meet the demand of a green wireless network, a dynamic gradient-aware hierarchical packet forwarding mechanism is designed. According to the relative positions of nodes, gradient-aware clusters are established. Consequently, considering the energy conversion efficiency and relative distance, cluster heads are selected reasonably. Furthermore, by exploiting the available energy and the number of cluster members, packets can be forwarded to the sink in an energy-efficient manner. Results show that the network lifetime can be noticeably improved.

Construction and analysis of a green clustered architecture for RNG-based wireless ad hoc networks

The limited battery power available in the nodes of an ad hoc network makes it mandatory to reduce the energy expenditure. Thus, reducing the transmission distances between nodes and balancing the number of nodes among clusters are two effective approaches to battery power conservation in the design of green wireless ad hoc networks. To realise this goal, the concept of relative neighbourhood graph RNG is used to convert the original physical network to an RNG-based logical network. Then, a green clustering algorithm GCA is proposed to reorganise the RNG-based logical network into a clustered architecture in which the number of cluster members among clusters is balanced. Finally, a first-order energy consumption model is applied to analyse the energy consumption in exchanging control messages and transmitting/receiving data packets. Through extensive simulations, it is confirmed that the proposed approaches effectively reduce energy consumption and, thus, extend the battery lifetime.

K-means based Clustering Method with a Fixed Number of Cluster Members

Clustering methods are very useful in many fields such as data mining, classification, and object recognition. Both the supervised and unsupervised grouping approaches can classify a series of sample data with a predefined or automatically assigned cluster number. However, there is no constraint on the number of elements for each cluster. Numbers of cluster members for each cluster obtained from clustering schemes are usually random. Thus, some clusters possess a large number of elements whereas others only have a few members. In some areas such as logistics management, a fixed number of members are preferred for each cluster or logistic center. Consequently, it is necessary to design a clustering method that can automatically adjust the number of group elements. In this paper, a k-means based clustering method with a fixed number of cluster members is proposed. In the proposed method, first, the data samples are clustered using the k-means algorithm. Then, the number of group elements is adjusted by employing a greedy strategy. Experimental results demonstrate that the proposed clustering scheme can classify data samples efficiently for a fixed number of cluster members.

Virtual Nodes-Based Wavelet Compression Algorithm for Wireless Sensor Networks

除了能量受限以外,有限的存储容量也是无线传感器网络的基本特征.研究传感器网络中节省存储的数据传输问题,提出了一种基于虚拟节点的渐进数据传输方法.首先定义虚拟节点并建立各级虚拟节点之间的对应关系,充分利用传感数据的相关性;然后,设计基于此映射关系的传感数据调度算法,单轮传送数据的节点总数由相应簇头的实际存储容量决定,虚拟节点对每轮收集到的数据进行联合编码,形成节省存储的渐进数据传输.模拟实验表明,所提出的算法比DIMENSIONS有更小的网络耗能和延时,而且具有存储有效性.

Identity-based cluster key agreement scheme in Ad Hoc network

In view of the characteristics of limited energy and dynamic change in Ad Hoc network,an identity-based group key agreement scheme was presented.The topology was in a structure composed by clusters,and allowed the synchronous execution of multi-party key agreement protocols based on pairings.The number of cluster members did not affect the key agreement,and it did not require interactivity during the key agreement.It provided the authentication and dynamics.In addition,the scheme was proved semantics secure under the Decisional Bilinear Diffie-Hellman(DBDH) problem.At last,compared with the previous schemes,the proposed scheme has advantages in terms of negotiation rounds and authentication.

ADAPTIVE ORDERED WEIGHTED AVERAGING FOR ANOMALY DETECTION IN CLUSTER-BASED MOBILE AD HOC NETWORKS

In this paper, an anomaly detection method in cluster-based mo- bile ad hoc networks with ad hoc on demand distance vector (AODV) routing protocol is proposed. In the method, the required features for describing the normal behavior of AODV are dened via step by step analysis of AODV and independent of any attack. In order to learn the normal behavior of AODV, a fuzzy averaging method is used for combining one-class support vector machine (OCSVM), mixture of Gaussians (MoG), and self-organizing maps (SOM) one-class classiers and the combined model is utilized to par- tially detect the attacks in cluster members. The votes of cluster members are periodically transmitted to the cluster head and nal decision on attack detec- tion is carried out in the cluster head. In the proposed method, an adaptive ordered weighted averaging (OWA) operator is used for aggregating the votes of cluster members in the cluster head. Since the network topology, trac, and environmental conditions of a MANET as well as the number of nodes in each cluster dynamically change, the mere use of a xed quantier-based weight generation approach for OWA operator is not ecient. We propose a condition-based weight generation method for OWA operator in which the number of cluster members that participate in decision making may be varying in time and OWA weights are calculated periodically and dynamically based on the conditions of the network. Simulation results demonstrate the eec- tiveness of the proposed method in detecting rushing, RouteError fabrication, and wormhole attacks.

An Energy-Efficient Unequal Clustering Algorithm in Wireless Sensor Networks

For wireless sensor networks, clustering algori-thms provides an effective way to prolong the life time using the multi-hop forwarding model. Nevertheless, they rarely consider the hot spots problem and the problem of unbalanced energy consumption among cluster heads. To solve the problems, we proposed an energy-efficient unequal clustering algorithm with the ideal of unequal clustering in the circle area where the cluster heads are in charge of different geographical scope according to different distance to the base station. Considering the cluster heads closer to the BS be burdened with heavy relay traffic, the cluster in inner layers, which is closer to the base station, is smaller than the outer layer. It could reduce the number of cluster members and lead to the proportional energy dissipation in each layer. Simulation results show that our algorithm improve energy utilization and prolonged the life of the entire Wireless Sensor Networks effectively.