Effect Of Cluster Size Research Articles

Performance Evaluation of Spectrum Efficiency of a Cellular Wireless Network in Nigeria (Apapa Lagos, as a Case Study)

Ever increasing demand for communication services has led to serious challenges to network operators to ensure acceptable Quality of Service (QoS) at minimum cost. The increasing number of subscribers and demand for greater variety of services make it difficult for network operators to provide the service varieties subscribers want while maintaining acceptable levels of quality of service. Spectrum is a precious commodity, knowing how efficiently different technologies and applications use it allows users and operators to make the best decisions on what wireless technology to deploy and in what configuration. Spectrum efficiency is an important parameter for accessing the frequency requirements of cellular mobile radio systems; radio channel capacity is a measure of the spectrum efficiency of a wireless system. The effect of cluster size, carrier to interference ratio and carried traffic in the spectrum efficiency are discussed. The fundamental figure of merit is the spectral efficiency was found to be 1.3698 fall within the regulator and defined items of Erlangs/MHz/Km2 proved to be adequate, comprehensive, and appropriate for cellular system. It is also observed that the smallest value of C/I ratio provides largest spectrum efficiency. Comparatively, the study of the 120° sectored and 60° sectored cells can be done in relation to non-sectored (omnidirectional, 360°) cells when a number of interfering base stations are considered. The performance of improvement of the 60° sectored provides better coverage performance than 120° sectored and Omni. Low C/I ratio will cause coverage issue including dropped calls, block calls, and other handset reception problem.

The Accuracy of Determining Cluster Size by Analyzing Ripley’s K Function in Single Molecule Localization Microscopy

Ripley’s K function was developed to analyze the spatial distribution characteristics in point pattern analysis, including geography, economics and biomedical research. In biomedical applications, it is popularly used to analyze the clusters of proteins on the cell plasma membrane in single molecule localization microscopy (SMLM), such as photo activated localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), universal point accumulation imaging in nanoscale topography (uPAINT), etc. Here, by varying the parameters of the simulated clusters on a modeled SMLM image, the effects of cluster size, cluster separation and protein ratio inside/outside the cluster on the accuracy of cluster analysis by analyzing Ripley’s K function were studied. Although the predicted radius of clusters by analyzing Ripley’s K function did not exactly correspond to the actual radius, we suggest the cluster radius could be estimated within a factor of 1.3. Employing peak analysis methods to analyze the experimental epidermal growth factor receptor (EGFR) clusters at fibroblast-like cell lines derived from monkey kidney tissue - COS7 cell surface observed by uPAINT method, the cluster properties were characterized with errors. Our results present quantification of clusters and can be used to enhance the understanding of clusters in SMLM.

Empirical Discovery of Power-Law Distribution in MapReduce Scalability

Understanding the scalability of MapReduce applications is a challenging problem. The difficulty lies in the distributed mapping of the input big data. The distribution of data and compute resources must match with fluctuating network substrates. User-defined Map and Reduce functions over application parameters further complicate the issue. Therefore, it offers great payoff to use small datasets and limited test runs to reveal the behavior of MapReduce applications over big-data. In this paper, we analyze the scaling effects of server cluster-size over varieties of Map- and Reduce-intensive applications. In our study, we discover specific conditions which lead to the power-law conformity in representative MapReduce applications. We report four major discoveries: (1) Within a range of scaling parameters, MapReduce execution time follows the power-law distribution. (2) Power-law scalability for Map-intensive applications work well even with a small cluster size. (3) Shuffle-intensive applications exhibit power-law behavior starting from larger cluster size. (4) The scaling effects may depart from power-law distribution, if the cloud resources are heavily overprovisioned than the workload demands. The above findings enable users to use bounded test runs to allocate and configure virtual and physical resources in large-scale MapReduce applications. These results can be also applied in generating business models for providing cost-effective cloud computing services.

Probing into the effects of cluster size and Pd ensemble as active center on the activity of H2 dissociation over the noble metal Pd-doped Cu bimetallic clusters

Aiming at probing into the effects of cluster size and Pd ensemble as active center on H2 dissociation over Pd-doped Cu bimetallic cluster, density functional theory calculations are employed to investigate H2 adsorption and dissociation over the Cu and Pd-doped Cu clusters with different sizes. The results show that compared to Cu cluster, the doped Pd atom and its ensemble composed of outermost layer and its connected sub-layer Pd atoms as active center greatly enhance catalytic activity of H2 dissociation at the same size of Pd-doped Cu clusters, which is attributed to the interaction enhancement between H2 and the clusters since d-band centers gradually approach the Fermi level and the electron-rich regions are formed around Pd ensemble. Moreover, among the Pd-doped Cu clusters with different sizes, the Bader charge indicate that when the number of doped-Pd atoms is same, the amount of charge on Pd ensemble increases with the size increasing of Pd-doped Cu catalysts, the cluster with larger size is beneficial for promoting the activity of H2 dissociation; thus, the size effect of Pd-doped Cu cluster on H2 dissociation is dominantly attributed to the electronic effect. This work not only provides a valuable clue for evaluating catalytic activity of other promoter metal-doped Cu catalysts for H2 dissociation, but also gives out the ways that adjusting the cluster size and the promoter ensemble can facilitate the dissociation of H2 atoms as an initially key step in the heterogeneous hydrogenation.

Determination of thermoelastic stress wave propagation in nanocomposite sandwich plates reinforced by clusters of carbon nanotubes

Adding small amounts of carbon nanotubes (CNTs) into the face sheets of sandwich structures can significantly improve their thermo-mechanical responses. However, the formation of CNT clusters, especially at high volume fractions of CNTs, dramatically affects the mechanical properties of the resulted nanocomposites, which is usually ignored. In this paper, by considering the formation of CNT clusters, we have investigated transient heat transfer and stress wave propagation in polymeric sandwich plates with two nanocomposite face sheets. The face sheets were made of clusters of CNTs embedded in a polymeric matrix. The volume fractions of CNTs and their clusters were assumed to be functionally graded along the thickness of face sheets. The proposed sandwich plate was subjected to thermal and impact pressure loads. Eshelby–Mori–Tanaka’s approach was applied to evaluate the material properties of the resulted nanocomposite with components with temperature-dependent material properties. Reddy’s third-order shear deformation theory and a moving least square shape function-based mesh-free method were utilized for thermoelastic dynamic analysis. The effects of CNT cluster size, distribution, and volume fraction as well as thermal load on the thermoelastic dynamic behavior of nanocomposite sandwich plates were investigated. It was observed that the distribution and cluster size of CNTs had significant effects on the amplitude and speed of thermoelastic stress wave propagation in the nanocomposite sandwich plates.

Acetylene selective hydrogenation over different size of Pd-modified Cu cluster catalysts: Effects of Pd ensemble and cluster size on the selectivity and activity

Aiming at identifying the effects of Pd ensemble and cluster size on the selectivity and activity of C2H2 selective hydrogenation over Pd-modified Cu nano-cluster catalysts, the catalytic performance of C2H2 selective hydrogenation over different cluster sizes of Pd-modified Cu catalyst with different Pd ensemble are examined using density functional theory calculations. A new evaluation method of the C2H4 selectivity is defined. The results indicate that the promoter Pd cannot improve C2H4 selectivity and activity over the small-sized Cu13 and the large-sized Cu55 clusters. Only when Cu catalyst has a moderate size such as Cu38 cluster, the Pd ensemble composed of outer shell with 6 coordination and its contiguous inner-layer Pd atoms can significantly improve C2H4 selectivity and activity. This study provides a theoretical clue for the design of highly efficient and cost-effective noble promoter-modified Cu-based catalysts by controlling the cluster size and the promoter ensemble for C2H2 selective hydrogenation.

First-principles studies on the doping effect of Nin−1TM (n = 13, 19, 55)

The structural and electronic properties of different transition-metal atoms (TM = Cr, Mn, Fe, Co, Cu, Zn, Pd, Ag, Pt, Au) doped into Nin neutral and ionic clusters (n = 13, 19, 55) were investigated using density functional theory calculations with the PBE exchange-correlation energy functional. The properties of the Nin−1TM clusters differed substantially from those of Nin, implying that the substitution of a transition-metal atom (TMA) strongly affected the stability and electronic properties of the resultant clusters. The cluster size effect and element type influence are discussed. The stability and the total magnetic moments of Nin−1TM neutral and ionic clusters were found to increase as a function of cluster size. The size of the clusters had little effect on the amount of charge transferred. The AIP decreased with increasing cluster size, while the AEA is exhibited the opposite trend. All of the aforementioned electronic properties exhibited different degrees of variation due to doping of different TMAs.

Hydrogen quenches the size effects in carbon clusters.

A characteristic fingerprint of atomic clusters is that their properties can vary in a non-smooth way with the cluster size N. This is illustrated herein by studying the cluster size dependence of several properties of neutral CN and cationic C+N carbon clusters: C-C bond lengths, cluster structure, intrinsic cluster stability, ionization energy, and spatial distribution of the reactivity index for charge exchange with electrophiles. Nonetheless, clusters can lose the size dependence of their properties by interaction with other chemical species, which is rationalized in this study by analyzing carbon clusters fully saturated with hydrogen to form linear alkanes, CNH2N+2. In all cases, the lowest energy structures are zigzagging linear chains, the variations of C-C bond lengths and angles with alkane size are very minor and smooth, the stability function shows practically no structure as a function of the alkane size, the ionization energies just decrease smoothly with alkane size, and the spatial distribution of the reactivity index is analogous and highly delocalized in all the alkanes. In summary, the interaction of carbon clusters with hydrogen to form alkanes quenches all the size-dependent features that the carbon clusters originally owned. The arrival at the quenching of the size effects follows an involved path. In each CNHn family with fixed N, the values of the properties of the molecules like the ionization potential, the electron affinity, and others show sizable oscillations as the number of hydrogen atoms grows from the pure carbon cluster to the alkane.

Effect of magnesium cluster size on Grignard reagent formation mechanism. A quantum-chemical study

The density functional theory (B3PW91/6-311[Formula: see text]G(2d,2p)) was used to obtain optimized geometry, energy and electronic properties of Mg[Formula: see text]EtBr adsorption complexes, Et[Formula: see text]Mg[Formula: see text]Br intermediates and transition states for ethyl bromide reaction with the magnesium atom and Mgn ([Formula: see text]–20) clusters modeling the distorted surface of the metallic magnesium. We suggest a consecutive multi-stage scheme of EtBr[Formula: see text]Mgn process in vacuum that includes adsorption of reagents on external cluster atoms, chemical interaction with Grignard reagent formation and desorption of products to the volume. Two fundamentally different reaction channels were investigated — the radical and molecular ones. Influence of Mgn cluster size on the preference of each of channels was established. It is shown that the reaction route is primarily determined with the position of the reactive group of atoms participating in formation of the transition state or radical intermediates and the activation energy of EtMgBr formation correlates with the energy of atom detachment from a cluster. For both pathways, reaction activation energies and thermodynamic parameters of the elementary steps were calculated.

Bayesian evaluation of informative hypotheses in cluster-randomized trials

Researchers often have informative hypotheses in mind when comparing means across treatment groups, such as H1 : μA < μB < μC and H2 : μB < μA < μC, and want to compare these hypotheses to each other directly. This can be done by means of Bayesian inference. This article discusses the disadvantages of the frequentist approach to null hypothesis testing and the advantages of the Bayesian approach. It demonstrates how to use the Bayesian approach to hypothesis testing in the setting of cluster-randomized trials. The data from a school-based smoking prevention intervention with four treatment groups are used to illustrate the Bayesian approach. The main advantage of the Bayesian approach is that it provides a degree of evidence from the collected data in favor of an informative hypothesis. Furthermore, a simulation study was conducted to investigate how Bayes factors behave with cluster-randomized trials. The results from the simulation study showed that the Bayes factor increases with increasing number of clusters, cluster size, and effect size, and decreases with increasing intraclass correlation coefficient. The effect of the number of clusters is stronger than the effect of cluster size. With a small number of clusters, the effect of increasing cluster size may be weak, especially when the intraclass correlation coefficient is large. In conclusion, the study showed that the Bayes factor is affected by sample size and intraclass correlation similarly to how these parameters affect statistical power in the frequentist approach of null hypothesis significance testing. Bayesian evaluation may be used as an alternative to null hypotheses testing.

Effects of particle clustering on the plastic deformation and damage initiation of particulate reinforced composite utilizing X-ray CT data and finite element modeling

In this paper, a new simulation technique which can include microstructural inhomogeneity of particulate reinforced composites is proposed to accurately study deformation pattern and damage mechanism in these composites. Three dimensional microstructures constructed from XCT images incorporated into finite element modeling codes with minimal approximation to capture the effects of cluster size, local volume fraction of particles in the cluster and the distance between clusters as relevant statistical quantities describing the microstructural inhomogeneity of particulate reinforced composites. A quantitative parameter as degree of clustering is defined to consider particle clustering effect. The results indicate that the damage growth rate of composite with higher degree of clustering is significantly higher than those composites with lower degree of clustering. It is found that for region with higher degree of clustering and bigger size of clusters, the von Mises stress is higher at the same loading condition and the growth rate of plastic flow is considerably higher than the other region with lower degree of clustering. Moreover, the dislocation description of deformation in two-phase materials rationalize particle clustering effect on the yield behavior of the particulate reinforced composites and the flow stress in these composites. The macroscopic stresses that lead to the initial yielding in the matrix decrease when clusters closely proximate with bigger size and higher degree of clustering.

Adsorption, Dissociation, and Spillover of Hydrogen over Au/TiO2 Catalysts: The Effects of Cluster Size and Metal–Support Interaction from DFT

The effects of cluster size and metal–support interaction on the catalytic activity of Au nanoparticles supported on anatase TiO2(101) and (001) surfaces for H2 adsorption, activation and dissociation were investigated by periodic density functional theory (DFT) calculations. On the stoichiometric TiO2 surfaces, it was found that the adsorptions of both Au clusters and H2 molecules are sensitive to the cluster size of gold, and the (001) facet with “soft” lattice and coordination unsaturated atoms on the surface is superior for Au adsorption stability, but H2 adsorption does not show apparent distinction on the two catalysts. The Au atoms active for H2 activation should be neutral in charge and located at the edge or corner of the Au nanoparticles in lower coordination. The metal–support interaction plays an important role for H2 activation and dissociation, and the O2––H+–H––Au structure was identified in the transition state through which H2 dissociation occurred via a heterolytic dissociation process a...

A log rank test for clustered data with informative within-cluster group size.

The log rank test is a popular nonparametric test for comparing survival distributions among groups. When data are organized in clusters of potentially correlated observations, adjustments can be made to account for within-cluster dependencies among observations, eg, tests derived from frailty models. Tests for clustered data can be further biased when the number of observations within each cluster and the distribution of groups within cluster are correlated with survival times, phenomena known as informative cluster size and informative within-cluster group size. In this manuscript, we develop a log rank test for clustered data that adjusts for the potentially biasing effect of informative cluster size and within-cluster group size. We provide the results of a simulation study demonstrating that our proposed test remains unbiased under cluster-based informativeness, while other candidate tests not accounting for the clustering structure do not properly maintain size. Furthermore, our test exhibits power advantages under scenarios in which traditional tests are appropriate. We demonstrate an application of our test by comparing time to functional progression between groups defined initial functional status in a spinal cord injury data set.

Catalytic upgrading of biomass-derived vapors on carbon aerogel-supported Ni: Effect of temperature, metal cluster size and catalyst-to-biomass ratio

A comprehensive study of carbon aerogel-supported nickel (Ni/CAG) in the catalytic fast pyrolysis (CFP) of torrefied Eucalyptus globulus was performed in a micropyrolysis unit (Py-GCMS). Effects of pyrolysis temperatures (450–600 °C), catalyst-to-biomass ratio (1:1 < C-to-B < 10:1) and metal cluster sizes (9.6 < Dpi < 21) on upgrading reactions were analyzed. Catalysts were characterized by N2 adsorption-desorption at 77 K, X-ray diffraction (XRD) and Transmission Electron Microscopy (TEM). Regardless the use of catalysts, the pyrolysis vapors produced from torrefied biomass were depleted in carboxylic acids (selectivity < 7%). Furthermore, the CFP decreased the selectivity to furans and ketones by almost 50%, while phenols increased in a similar proportion. Despite the similarities in the selectivity distribution of these functional groups in all the CFP experiments, their internal composition was rather different owing to the changes in temperature, C-to-B ratio and catalyst cluster sizes. Ni/CAG was active for hydrogenation under H2-depleted atmosphere, presumably by a synergistic effect between water gas shift and reforming reactions with transalkylation and decarbonylation of phenolics and furanics. It was demonstrated that metal cluster sizes influenced the reaction routes by favoring hydrogenation on metal facets and deoxygenation on step/corners sites.

Perspective: Size selected clusters for catalysis and electrochemistry.

Size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization, and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition, cluster-support interactions, and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modeling based on density functional theory sampling of local minima and energy barriers or ab initio molecular dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Finally, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.

Wiggler magnetic field assisted third harmonic generation in expanding clusters

A simple theoretical model is constructed to study the wiggler magnetic field assisted third harmonic generation of intense short pulse laser in a cluster in its expanding phase. The ponderomotive force of laser causes density perturbations in cluster electron density which couples with wiggler magnetic field to produce a nonlinear current that generates transverse third harmonic. An intense short pulse laser propagating through a gas embedded with atomic clusters, converts it into hot plasma balls via tunnel ionization. Initially, the electron plasma frequency inside the clusters ωpe > (with ω1 being the frequency of the laser). As the cluster expands under Coulomb force and hydrodynamic pressure, ωpe decreases to . At this time, there is resonant enhancement in the efficiency of the third harmonic generation. The efficiency of third harmonic generation is enhanced due to cluster plasmon resonance and by phase matching due to wiggler magnetic field. The effect of cluster size on the expansion rate is studied to observe that the clusters of different radii would expand differently. The impact of laser intensity and wiggler magnetic field on the efficiency of third harmonic generation is also explored.

Ion induced electron emission statistics under Agm- cluster bombardment of Ag

Abstract The electron emission from a polycrystalline silver surface under bombardment with Ag m - cluster ions ( m = 1 , 2 , 3 ) is investigated in terms of ion induced kinetic excitation. The electron yield γ is determined directly by a current measurement method on the one hand and implicitly by the analysis of the electron emission statistics on the other hand. Successful measurements of the electron emission spectra ensure a deeper understanding of the ion induced kinetic electron emission process, with particular emphasis on the effect of the projectile cluster size to the yield as well as to emission statistics. The results allow a quantitative comparison to computer simulations performed for silver atoms and clusters impinging onto a silver surface.

The effects of charge, polymerization, and cluster size on the diffusivity of dissolved Si species in pore water

Silicon (Si) is the most abundant cation in crustal rocks. The charge and degree of polymerization of dissolved Si significantly change depending on solution pH and Si concentration. We used molecular dynamics (MD) simulations to predict the self-diffusion coefficients of dissolved Si, DSi, for 15 monomeric and polymeric species at ambient temperature. The results showed that DSi decreased with increasing negative charge and increasing degree of polymerization. The relationship between DSi and charge (Z) can be expressed by DSi/10−6 = 2.0 + 9.8e0.47Z, and that between DSi and number of polymerization (NSi) by DSi/10−6 = 9.7/NSi0.56. The results also revealed that multiple Si molecules assembled into a cluster and D decreased as the cluster size increased. Experiments to evaluate the diffusivity of Si in pore water revealed that the diffusion coefficient decreased with increasing Si concentration, a result consistent with the MD simulations. Simulation results can now be used to quantitatively assess water-rock interactions and water-concrete reactions over a wide range of environmentally relevant conditions.

A structure sensitivity approach to temperature programmed desorption

A novel approach for explaining the effect of the metal cluster size on Temperature Programmed Desorption (TPD) from supported catalysts is described. Based on the assumption that metal clusters have regular cuboctahedral geometry, it was possible to develop a model that describes how three dimensional aspects of the metal clusters may affect the TPD profiles. Depending on the way that the metal clusters are assumed to be arranged in the surface, two different approaches are described to account for desorption. The first one, named Monocluster Model, considers a surface with a narrow cluster size distribution in which an average cluster diameter reasonably well describes the cluster population. A more complete approach, named Multicluster Model, explains desorption based on the cluster size distribution of the metal sites. TPD curves of ammonia from different platinum supported carbon materials were numerically treated and compared with the geometrical information obtained from Transition Electron Microscopy. The results displayed a reasonable agreement, indicating that TPD curves should be interpreted taking into account the metal cluster size.

Unequal cluster sizes in stepped-wedge cluster randomised trials: a systematic review

ObjectivesTo investigate the extent to which cluster sizes vary in stepped-wedge cluster randomised trials (SW-CRT) and whether any variability is accounted for during the sample size calculation and analysis of...