Properties Of Clusters Research Articles

Influencing mechanisms of tartaric acid on adsorption and degradation of tetracycline on goethite: insight from solid and liquid aspects.

The interactions between organic pollutants and iron minerals play an important role in their environmental fate. In this study, the effects of low-molecular-weight organic acids (LMWOAs) on the adsorption and degradation of tetracycline (TC) on goethite were investigated. Tartaric acid (TA) was taken as the representative of LMWOAs to study the influencing mechanism through batch experiments and microscale characterization. In addition, the properties of TC-TA clusters under different pHs were determined by density functional theory (DFT) calculations. The results showed that all five LMWOAs inhibited TC adsorption and degradation. The preferential adsorption of TA on goethite changed TC adsorption from inner spherical to outer spherical complexation and mainly inhibited TC adsorption and degradation of the singly coordinated hydroxyl group. TC degradation rate decreased from 0.0287 to 0h-1 in the first stage. Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy results showed that TA could influence the interactions of amide groups, C = O on the A-ring, and dimethylamino group of TC with goethite, and the formation of ≡Fe(II) was inhibited. In addition to competing for the effective sites, the effects of complexation between TA and TC in solution should be considered. According to DFT calculations, hydrogen bonds could be formed between the carboxyl group of TA and the H atom of TC at different pH. These findings can provide evidence for estimating the contribution of adsorption and degradation to TC removal by iron oxides with the coexistence of LMWOAs in a soil-water environment.

Predicting Binding Energies and Electronic Properties of Boron Nitride Fullerenes Using a Graph Convolutional Network.

As isoelectronic counterparts of carbon fullerenes, medium-sized boron nitride clusters also prefer cage structures composed of even-sized polygons. As the cluster size increases, the number of cage isomers grows rapidly, and determining the ground state structure requires a tremendous amount of DFT calculations. Herein, we develop a graph convolutional network (GCN) that can describe the energy of a (BN)n cage by its topology connection. We define a vertex feature vector on a dual polyhedron by the permutation of the neighbor vertices' degree and aggregate the information on vertices by two graph convolutional layers to learn the local feature of the dual polyhedron. The GCN is trained on (BN)28 and subsequently tested on (BN)23 and (BN)24 data sets, which satisfactorily reproduce the order of isomer energies from DFT calculations. We further employ the trained GCN to predict the ground state structures within the size range of n = 25-32, which agree well with DFT results. Using the same GCN framework, we also successfully trained the highest-occupied or lowest-unoccupied orbital energies of (BN)28 isomers. The present graph convolutional network establishes a direct mapping between the topological connection and the energetic or electronic properties of a cage-like cluster or molecule.

Influence of Grapevine Cultivar on Population Levels of Lobesia botrana (Lepidoptera: Tortricidae) and Effectiveness of Insecticides in Controlling This Pest

The European grapevine moth, Lobesia botrana (Denis and Schiffermüller) (Lepidoptera: Tortricidae), is the most critical pest of vineyards. In the present study, pheromone-baited traps were applied in 2021 and 2022 to monitor the moth population dynamics and to determine the number of L. botrana generations. The number of eggs and larvae was also counted in four vineyards with Askari, Yaghooti, Keshmeshi, and Fakhri cultivars. Moreover, the morphological properties of clusters were evaluated in different grape cultivars to find out the susceptible cultivar to L. botrana. In 2022, different insecticides were used in the Askari cultivar vineyard, and larval damage level was assessed. Three generations were recorded in all vineyards each year. The population of males was not affected by the cultivar. In contrast, the population density of eggs and larvae was significantly higher in Yaghooti than in other tested cultivars. It could be attributed to the cluster compactness and thin skin of berries in Yaghooti, which makes it more susceptible to L. botrana infestations. In contrast, the lowest eggs and larval population density was reported in the Fakhri cultivar indicating the tolerance of this cultivar compared to the other tested cultivars. The field trial showed that the application of insecticides in the second and third generations reduced the damage level of L. botrana. The rotation of insecticides with different modes of action in consecutive generations of L. botrana can be used to reduce damage levels.

Coupling particle image velocimetry and digital image analysis to characterize cluster dynamics in a fast fluidized bed

Particle clusters affect interphase interaction and dominate the overall performance of gas-solid fluidized beds. In this work, the particle image velocimetry, particle tracking velocimetry and digital image analysis were coupled to characterize clustering dynamics in a lab-scale riser. The clusters were identified by the Otsu algorithm based on grayscale distribution, and the cluster properties such as equivalent size, solids concentration and velocity distribution were extracted and analyzed under various operating conditions. It was found macroscopic operating conditions affect cluster characteristics significantly whereas the effect of particle composition is secondary. A bimodal or skewed probability density function was observed for not only the time-averaged hydrodynamic velocity but also the particle velocity around the cluster interface, indicating the coexistence of dilute and dense phases and the breaking of equilibrium assumption. The nonuniform distributions for laminar and Reynolds-stress-like granular temperatures were further discussed, which show remarkable anisotropy at both the microscopic and mesoscopic scales.

Topological approach and analysis of clustering in consensus networks

We study clustering properties of networks of single-integrator nodes over a directed graph, in which the nodes converge to steady-state values. These values define clustering groups of nodes, which are considered dependent on interaction topology and edge weights. Focusing on the interaction topology of the network, in this paper, we introduce the notion of topological clusters, which are sets of nodes that converge to an identical value due to the topological characteristics of the network, independent of the value of the edge weights. We then investigate properties of topological clusters and present a necessary and sufficient condition for a set of nodes to form a topological cluster. We also provide an algorithm for finding topological clusters, which is validated by an example.

Spatial clustering based gene selection for gene expression analysis in microarray data classification

A typical application of categorization in data mining is to uncover interesting distributions and significant patterns in the information that underlies it using density-based spatial clustering for workloads with noise. In these conditions, it is anticipated that the classification of the microarray gene expression database will have the necessary clustering property that may be utilized to emphasize the effects of the alterations. The proposed method typically guarantees that the subsequent identification of gene clusters’ best global arrangement of genes. It provides an iterative method for figuring out the precise number of clusters needed for each data collection. The technique is based on practices frequently used in statistical tests. The key idea is to coordinate gene redistribution optimization across clusters with the search for the optimal number of groups. An experiment that finds the most effective number of genes over time was used to evaluate the effectiveness of the suggested strategy. It used this stringent statistical test to show that our technique accurately clusters more than 95% of the genes. Finally, since the basic principles of gene development and gene cluster assignment have been well characterized by earlier studies and the technique was verified using real gene expression information.

Density functional theory calculations on the structures and electronic properties of boron nitride clusters toward formaldehyde

Density functional theory calculations on the structures and electronic properties of boron nitride clusters toward formaldehyde

Theoretical aspects of the genesis of composites: Dependence of properties on the history of formation

AbstractStudies of the last decades have brought the results of modern natural science into technology of composite materials of the widest range. The methods of percolation theory have become constructive tools for studying the structure of composites, for the purposeful creation of materials with desired properties. In this paper, a computer model of the processes of self‐organization of percolation clusters has been created. The model considers such problems with self‐organization, in which the properties of a percolation cluster are also determined by the history of the system's development. To study the structure and properties of such systems, a three‐dimensional continuum percolation problem with interacting elements is solved in the model. Iterative algorithms for the motion and interaction of particles and clusters, leading to the formation of percolation systems, have been developed. Their structure and properties have been studied, a number of standard parameters have been calculated, and the dependence of the structure and properties of clusters on the rate of system generation, degree of self‐organization, and characteristic values of the correlation length has been studied; analytical dependences of a number of parameters of percolation systems are obtained.

Size Dependence of the Adsorption Properties of Nickel Clusters on the Surface of Aluminum Oxide

Size Dependence of the Adsorption Properties of Nickel Clusters on the Surface of Aluminum Oxide

STRUCTURES, ELECTRONIC PROPERTIES AND DIPOLE MAGNITUDES OF TMWn−1O3n AND TM2Wn−2O3n (TM = Ni, Pd AND Pt; n=2−6) CLUSTERS

Transition metal substitution can expand the applications of tungsten oxides in chemical sensors, catalysts, and other fields. In order to highlight the effect of TM substitution on the electronic properties of tungsten oxide clusters, the configurations, electronic properties and dipole magnitudes of [Formula: see text] and [Formula: see text] (TM = Ni, Pd and Pt; [Formula: see text]) clusters have been investigated via the density functional theory. The structures of [Formula: see text] and [Formula: see text] clusters inherit those of [Formula: see text] ([Formula: see text] = 2–6) clusters to some extent except for [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] clusters. [Formula: see text] clusters exhibit higher thermodynamic stability than the other [Formula: see text] and [Formula: see text] clusters. [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] clusters display more kinetically stable than adjacent [Formula: see text] and [Formula: see text] clusters. The Mülliken charge transfer amount of [Formula: see text] clusters is more than that of other [Formula: see text] and [Formula: see text] clusters except for [Formula: see text] clusters.

Particle dispersion and clustering in surface ocean turbulence with ageostrophic dynamics

Upper-ocean turbulent flows at horizontal length scales smaller than the deformation radius depart from geostrophic equilibrium and develop important vertical velocities, which are key to marine ecology and climatic processes. Due to their small size and fast temporal evolution, these fine scales are difficult to measure during oceanographic campaigns. Instruments such as Lagrangian drifters have provided another way to characterize these scales through the analysis of pair-dispersion evolution and have pointed out striking particle convergence events. By means of numerical simulations, we investigate such processes in a model of surface-ocean turbulence that includes ageostrophic motions. This model originates from a Rossby-number expansion of the primitive equations and reduces to the surface quasi-geostrophic model, a paradigm of submesoscale dynamics, in the limit of vanishing Rossby number. We focus on the effect of the ageostrophic dynamics on the pair-dispersion and clustering properties of Lagrangian tracer particles at the ocean surface. Our results indicate that while over long times the pair separation process is barely affected by the ageostrophic component of the velocity field, the latter is responsible for the formation of temporary particle aggregates, and the intensity of this phenomenon increases with the Rossby number. We further show that Lagrangian tracers preferentially accumulate in cyclonic frontal regions, which is in agreement with observations and other more realistic modeling studies. These findings appear interesting to improve the understanding of the turbulent transport by ocean fine scales and in light of upcoming, new high-resolution satellite data of surface velocity fields.

Simultaneous Estimates of Star-cluster Age, Metallicity, Mass, and Extinction (SESAMME). I. Presenting an MCMC Approach to Spectral Stellar Population Fitting

We present the first version release of SESAMME, a public, Python-based full spectrum fitting tool for Simultaneous Estimates of Star-cluster Age, Metallicity, Mass, and Extinction. SESAMME compares an input spectrum of a star cluster to a grid of stellar population models with an added nebular continuum component, using Markov Chain Monte Carlo methods to sample the posterior probability distribution in four dimensions: cluster age, stellar metallicity Z, reddening E(B − V), and a normalization parameter equivalent to a cluster mass. SESAMME is highly flexible in the stellar population models that it can use to model a spectrum; our testing and initial science applications use both BPASS and Starburst99. We illustrate the ability of SESAMME to recover accurate ages and metallicities even at a moderate signal-to-noise ratio (S/N ∼ 3–5 per wavelength bin) using synthetic, noise-added model spectra of young star clusters. Finally, we test the consistency of SESAMME with other age and metallicity estimates from the literature using a sample of Hubble Space Telescope/Cosmic Origins Spectrograph far-UV spectra toward young, massive clusters in M83 and NGC 1313. We find that, on the whole, SESAMME infers star cluster properties that are consistent with the literature in both low- and high-metallicity environments.

Accelerated binary black holes in globular clusters: forecasts and detectability in the era of space-based gravitational-wave detectors

ABSTRACT The motion of the centre of mass of a coalescing binary black hole (BBH) in a gravitational potential, imprints a line-of-sight acceleration (LOSA) on to the emitted gravitational-wave (GW) signal. The acceleration could be sufficiently large in dense stellar environments, such as globular clusters (GCs), to be detectable with next-generation space-based detectors. In this work, we use outputs of the cluster monte carlo (cmc) simulations of dense star clusters to forecast the distribution of detectable LOSAs in DECIGO and LISA eras. We study the effect of cluster properties – metallicity, virial and galactocentric radii – on the distribution of detectable accelerations, account for cosmologically motivated distributions of cluster formation times, masses, and metallicities, and also incorporate the delay time between the formation of BBHs and their merger in our analysis. We find that larger metallicities provide a larger fraction of detectable accelerations by virtue of a greater abundance of relatively lighter BBHs, which allow a higher number of GW cycles in the detectable frequency band. Conversely, smaller metallicities result in fewer detections, most of which come from relatively more massive BBHs with fewer cycles but larger LOSAs. We similarly find correlations between the virial radii of the clusters and the fractions of detectable accelerations. Our work, therefore, provides an important science case for space-based GW detectors in the context of probing GC properties via the detection of LOSAs of merging BBHs.

Anisotropic Star Clusters around Recoiling Supermassive Black Holes

Gravitational-wave recoil kicks from merging supermassive black hole binaries can have a profound effect on the surrounding stellar population. In this work, we study the dynamic and kinematic properties of nuclear star clusters following a recoil kick. We show that these postkick structures present unique signatures that can provide key insight to observational searches for recoiling supermassive black holes. In our previous paper, we showed that an in-plane recoil kick turns a circular disk into a lopsided, eccentric disk such as the one we observe in the Andromeda nucleus. Building on this work, here we explore many recoil kick angles as well as initial stellar configurations. For a circular disk of stars, an in-plane kick causes strong apsidal alignment with a significant fraction of the disk becoming retrograde at large radii. If the initial orbits are highly eccentric, an in-plane kick forms a bar-like structure made up of two antialigned lopsided disks. An out-of-plane kick causes clustering in the argument of periapsis, ω, regardless of the initial eccentricity distribution. Initially, isotropic configurations form anisotropies in the form of a torus of eccentric orbits oriented perpendicular to the recoil kick. Postkick surface density and velocity maps are presented in each case to highlight the distinct, observable structures of these systems.

CSST WL preparation I: forecast the impact from non-Gaussian covariances and requirements on systematics control

ABSTRACT The precise estimation of the statistical errors and accurate removal of the systematical errors are the two major challenges for the stage IV cosmic shear surveys. We explore their impact for the China Space Station Telescope (CSST) with survey area ${\sim} 17\,500\deg ^2$ up to redshift ∼4. We consider statistical error contributed from Gaussian covariance, connected non-Gaussian covariance, and super-sample covariance. We find the non-Gaussian covariances, which is dominated by the super-sample covariance, can largely reduce the signal-to-noise ratio of the two-point statistics for CSST, leading to an ∼1/3 loss in the figure of merit for the matter clustering properties (σ8–Ωm plane) and 1/6 in the dark energy equation of state (w0–wa plane). We further put requirements of systematics mitigation on intrinsic alignment of galaxies, baryonic feedback, shear multiplicative bias, and bias in the redshift distribution, for an unbiased cosmology. The 10−2–10−3 level requirements emphasize strong needs in related studies, to support future model selections and the associated priors for the nuisance parameters.

A Computational Exploration of Exohedrally Transition Metal Doped Si94- Superatom Based Magnetic MSi9M' Clusters (M, M' = Sc(II) to Cu(II)).

Nanosized clusters are drawing immense attention of the scientific community due to their size and composition dependent tunability of physical and chemical properties. Silicon nanoclusters are especially important because of their abundance and ample utility in the domains of electronics and semiconductor industry. Zintl phases of Si offer an excellent opportunity in the domain of nanocluster research owing to their superior stability and multifarious possibilities of tunability of electronic properties through doping with other elements. Doping silicon clusters with transition elements is a prevalent strategy to induce magnetic properties in such clusters. Although doping silicon clusters with single transition metal atoms can induce significant magnetism in nanoclusters, the dominant covalent interaction between silicon and the transition metal causes the magnetic moment to quench. The rational strategy of inducing a sustainable magnetic moment can be to introduce ferromagnetic interaction between two sites carrying nonvanishing magnetic moments. In the present work, such a possibility is explored in terms of the stability of the clusters and corresponding magnetic exchange coupling in them. The Si94-superatomic cluster is doped with two transition metal atoms exohedrally and the neutral clusters designed thereby are investigated computationally if they reduce or reinforce the high stability of the superatom and substantiate the possibility of obtaining nanosized magnetic units as building blocks of tunable materials for various applications.

Electric field effect on [Formula: see text] clusters for applications in MOSFETs and DSSCs: a DFT study.

The structural, electronic, non-linear optical (NLO) and spectral properties of [Formula: see text] clusters with [Formula: see text] have been studied under the influence of an external electric field using density functional theory (DFT). The effect of variation in the Hf:Ti ratio on different properties of clusters is investigated. The motivation to study [Formula: see text] clusters lies in the fact that HfTiO thin films have wide applications in various optoelectronic and photovoltaic devices. So, it will be interesting to study the effect of electric field on [Formula: see text] clusters with the variation in the number of Hf, Ti and O atoms. It is observed that out of all the clusters, [Formula: see text] and [Formula: see text] are the most stable clusters with high values of binding energy and HOMO-LUMO gap. The application of an external electric field on these most stable clusters distorts their geometry and their HOMO-LUMO gap decreases, dipole moment and polarizability increases as the electric field is increased from 0 a.u. to 340 x[Formula: see text] a.u. The applied electric field increases the polar character of clusters due to electron cloud deformation and hence, increases the reactivity of the clusters, thus making these clusters suitable for electrocatalytic reactions. The electric field controlled high values of dielectric constant makes these clusters suitable to be used in the oxide layer of metal oxide semiconductor field effect transistors (MOSFETs) with better capacitance. Under the effect of an electric field, the absorption peaks of UV-VIS spectra gets red-shifted. Due to the tuning of absorption spectra from ultraviolet to visible region, [Formula: see text] clusters can be thought of as a good replacement for [Formula: see text] in dye-sensitized solar cells (DSSCs). The computational study of [Formula: see text] clusters has been performed using DFT. For the ground state of [Formula: see text] clusters, the optimization and frequency calculations have been performed using hybrid B3LYP (Becke three-parameter exchange functional combined with Lee, Yang and Parr correlation functional) functional with LANL2DZ (Los Alamos National Laboratory effective core potentials with Double Zeta atomic set) basis set under hybrid-GGA (generalized gradient approximation). Optimization and frequency calculations have been performed in each case. The excited state calculations have been carried out within time-dependent DFT formalism for a total of 50 excited states. The computational chemistry software package Gaussian 16 along with its graphical interface Gaussview have been employed for the current study of [Formula: see text] clusters.

ExDe: Design space exploration of scheduler architectures and mechanisms for serverless data-processing

Serverless computing is increasingly used for data-processing applications in both science and business domains. At the core of serverless data-processing systems is the scheduler, which ensures dynamic decisions about task and data placement. Due to the variety of user, cluster, and workload properties, the design space for high-performance and cost-effective scheduling architectures and mechanisms is vast. The large design space is difficult to explore and characterize. To help the system designer disentangle this complexity, we present ExDe, a framework to systematically explore the design space of scheduling architectures and mechanisms. The framework includes a conceptual model and a simulator to assist in design space exploration.We use the framework, and real-world workloads, to characterize the performance of three scheduling architectures and two mechanisms. Our framework is open-source software available on Zenodo.

Asymptotic properties of hierarchical clustering in high-dimensional settings

In this study, three asymptotic behaviors of hierarchical clustering are defined and studied with strict conditions under several asymptotic settings, from large samples to high dimensionality, when having two independent populations. We proceed with the current comprehension of the asymptotic properties of hierarchical clustering in high-dimensional, low-sample-size (HDLSS) settings. For high-dimensional data, the asymptotic properties of hierarchical clustering are demonstrated under mild and practical settings, and we present simulation studies and hierarchical clustering performance discussions. Furthermore, hierarchical clustering was theoretically investigated when both the dimension and sample size approach infinity, and we generalized a latent number of populations considering hierarchical clustering in multiclass HDLSS settings.

Vasodilators activate the anion channel TMEM16A in endothelial cells to reduce blood pressure.

Systemic blood pressure is acutely controlled by total peripheral resistance as determined by the diameter of small arteries and arterioles, the contractility of which is regulated by endothelial cells lining the lumen of blood vessels. We investigated the physiological functions of the chloride (Cl-) channel TMEM16A in endothelial cells. TMEM16A channels generated calcium (Ca2+)-activated Cl- currents in endothelial cells from control (TMEM16Afl/fl) mice that were absent in those from mice with tamoxifen-inducible, endothelial cell-specific knockout of TMEM16A (TMEM16A ecKO). TMEM16A currents in endothelial cells were activated by the muscarinic receptor agonist acetylcholine and an agonist of the Ca2+ channel TRPV4, which localized in nanoscale proximity with TMEM16A as assessed by single-molecule localization imaging of endothelial cells. Acetylcholine stimulated TMEM16A currents by activating Ca2+ influx through surface TRPV4 channels without altering the nanoscale properties of TMEM16A and TRPV4 surface clusters or their colocalization. In pressurized arteries, activation of TMEM16A channels in endothelial cells induced by acetylcholine; TRPV4 channel stimulation; or intraluminal ATP, another vasodilator, produced hyperpolarization and dilation. Furthermore, deficiency of TMEM16A channels in endothelial cells resulted in increased systemic blood pressure in conscious mice. These data indicate that vasodilators stimulate TRPV4 channels, leading to Ca2+-dependent activation of nearby TMEM16A channels in endothelial cells to produce arterial hyperpolarization, vasodilation, and reduced blood pressure. Thus, TMEM16A is an anion channel in endothelial cells that regulates arterial contractility and blood pressure.