Instantaneous Cluster Research Articles

Spectroscape enables real-time query and visualization of a spectral archive in proteomics

In proteomics, spectral archives organize the enormous amounts of publicly available peptide tandem mass spectra by similarity, offering opportunities for error correction and novel discoveries. Here we adapt an indexing algorithm developed by Facebook for organizing online multimedia resources to tandem mass spectra and achieve practically instantaneous retrieval and clustering of approximate nearest neighbors in a large spectral archive. An interactive web-based graphical user interface enables the user to view a query spectrum in its clustered neighborhood, which facilitates contextual validation of peptide identifications and exploration of the dark proteome.

Star Formation Efficiency and Dispersal of Giant Molecular Clouds with UV Radiation Feedback: Dependence on Gravitational Boundedness and Magnetic Fields

Abstract Molecular clouds are supported by turbulence and magnetic fields, but quantifying their influence on cloud life cycle and star formation efficiency (SFE) remains an open question. We perform radiation magnetohydrodynamic simulations of star-forming giant molecular clouds (GMCs) with UV radiation feedback, in which the propagation of UV radiation via ray tracing is coupled to hydrogen photochemistry. We consider 10 GMC models that vary in either initial virial parameter (1 ≤ α vir,0 ≤ 5) or dimensionless mass-to-magnetic flux ratio (0.5 ≤ μ Φ,0 ≤ 8 and ∞ ); the initial mass 105 M ⊙ and radius 20 pc are fixed. Each model is run with five different initial turbulence realizations. In most models, the duration of star formation and the timescale for molecular gas removal (primarily by photoevaporation) are 4–8 Myr. Both the final SFE (ε *) and time-averaged SFE per freefall time (ε ff) are reduced by strong turbulence and magnetic fields. The median ε * ranges between 2.1% and 9.5%. The median ε ff ranges between 1.0% and 8.0%, and anticorrelates with α vir,0, in qualitative agreement with previous analytic theory and simulations. However, the time-dependent α vir(t) and ε ff,obs(t) based on instantaneous gas properties and cluster luminosity are positively correlated due to rapid evolution, making observational validation of star formation theory difficult. Our median ε ff,obs(t) ≈ 2% is similar to observed values. We show that the traditional virial parameter estimates the true gravitational boundedness within a factor of 2 on average, but neglect of magnetic support and velocity anisotropy can sometimes produce large departures from traditional virial parameter estimates. Magnetically subcritical GMCs are unlikely to represent sites of massive star formation given their unrealistic columnar outflows, prolonged lifetime, and low escape fraction of radiation.

The Photovoltaic Output Prediction Based on Variational Mode Decomposition and Maximum Relevance Minimum Redundancy

Photovoltaic output is affected by solar irradiance, ambient temperature, instantaneous cloud cluster, etc., and the output sequence shows obvious intermittent and random features, which creates great difficulty for photovoltaic output prediction. Aiming at the problem of low predictability of photovoltaic power generation, a combined photovoltaic output prediction method based on variational mode decomposition (VMD), maximum relevance minimum redundancy (mRMR) and deep belief network (DBN) is proposed. The method uses VMD to decompose the photovoltaic output sequence into modal components of different characteristics, and determines the main characteristic factors of each modal component by mRMR, and the DBN model is used to fit the modal components and the corresponding characteristic factors, then the predicted results of each modal component is superimposed to obtain the predicted value of the photovoltaic output. By using the data of a certain photovoltaic power station in Yunnan for comparative experiments, it is found that the model proposed in this paper improves the prediction accuracy of photovoltaic output.

The evolution of the UV luminosity function of globular clusters in the E-MOSAICS simulations

We present the evolution of the rest-frame ultraviolet (UV) properties of the globular cluster (GC) populations and their host galaxies formed in the E-MOSAICS suite of cosmological hydrodynamical simulations. We compute the luminosities of all clusters associated with 25 simulated Milky Way-mass galaxies, discussed in previous works, in the rest-frame UV and optical bands by combining instantaneous cluster properties (age, mass, metallicity) with simple stellar population models, from redshifts $z=0$ to 10. Due to the rapid fading of young stellar populations in the UV, most of the simulated galaxies do not host GCs bright enough to be individually identified in deep Hubble Space Telescope (HST) observations, even in highly magnified systems. The median age of the most UV-luminous GCs is $<10$ Myr (assuming no extinction), increasing to $\gtrsim 100$ Myr for red optical filters. We estimate that these GCs typically only contribute a few per cent of the total UV luminosity of their host galaxies at any epoch. We predict that the number density of UV-bright proto-GCs (or cluster clumps) will peak between redshifts $z=1-3$. In the main progenitors of Milky Way-mass galaxies, $10$-$20$ per cent of the galaxies at redshifts $1 \lesssim z \lesssim 3$ have clusters brighter than $M_{\rm UV} < -15$, and less than $10$ per cent at other epochs. The brightest cluster in the galaxy sample at $z>2$ is typically $M_{\rm UV} \sim -16$, consistent with the luminosities of observed compact, high-redshift sources.

Interaction of droplet dispersion and evaporation in a polydispersed spray

The interaction between droplet dispersion and evaporation in an acetone spray evaporating under ambient conditions is experimentally studied with an aim to understand the physics behind the spatial correlation between the local vapour mass fraction and droplets. The influence of gas-phase turbulence and droplet–gas slip velocity of such correlations is examined, while the focus is on the consequence of droplet clustering on collective evaporation of droplet clouds. Simultaneous and planar measurements of droplet size, velocity and number density, and vapour mass fraction around the droplets, were obtained by combining the interferometric laser imaging for droplet sizing and planar laser induced fluorescence techniques (Sahu et al., Exp. Fluids, vol. 55, 1673, 2014b, pp. 1–21). Comparison with droplet measurements in a non-evaporating water spray under the same flow conditions showed that droplet evaporation leads to higher fluctuations of droplet number density and velocity relative to the respective mean values. While the mean droplet–gas slip velocity was found to be negligibly small, the vaporization Damköhler number ($Da_{v}$) was approximately ‘one’, which means the droplet evaporation time and the characteristic time scale of large eddies are of the same order. Thus, the influence of the convective effect on droplet evaporation is not expected to be significant in comparison to the instantaneous fluctuations of slip velocity, which refers to the direct effect of turbulence. An overall linearly increasing trend was observed in the scatter plot of the instantaneous values of droplet number density ($N$) and vapour mass fraction ($Y_{F}$). Accordingly, the correlation coefficient of fluctuations of vapour mass fraction and droplet number density ($R_{n\ast y}$) was relatively high (${\approx}0.5$) implying moderately high correlation. However, considerable spread of the $N$ versus $Y_{F}$ scatter plot along both coordinates demonstrated the influence on droplet evaporation due to turbulent droplet dispersion, which leads to droplet clustering. The presence of droplet clustering was confirmed by the measurement of spatial correlation coefficient of the fluctuations of droplet number density for different size classes ($R_{n\ast n}$) and the radial distribution function (RDF) of the droplets. Also, the tendency of the droplets to form clusters was higher for the acetone spray than the water spray, indicating that droplet evaporation promoted droplet grouping in the spray. The instantaneous group evaporation number ($G$) was evaluated from the measured length scale of droplet clusters (by the RDF) and the average droplet size and spacing in instantaneous clusters. The mean value of $G$ suggests an internal group evaporation mode of the droplet clouds near the spray centre, while single droplet evaporation prevails near the spray boundary. However, the large fluctuations in the magnitude of instantaneous values of $G$ at all measurement locations implied temporal variations in the mode of droplet cloud evaporation.

Star Cluster Formation from Turbulent Clumps. I. The Fast Formation Limit

Abstract We investigate the formation and early evolution of star clusters, assuming that they form from a turbulent starless clump of a given mass bounded inside a parent self-gravitating molecular cloud characterized by a particular mass surface density. As a first step, we assume instantaneous star cluster formation and gas expulsion. We draw our initial conditions from observed properties of starless clumps. We follow the early evolution of the clusters up to 20 Myr, investigating the effects of different star formation efficiencies, primordial binary fractions and eccentricities, and primordial mass segregation levels. We investigate clumps with initial masses of embedded in ambient cloud environments with mass surface densities and . We show that these models of fast star cluster formation result, in the fiducial case, in clusters that expand rapidly, even considering only the bound members. Clusters formed from higher environments tend to expand more quickly and thus are soon larger than clusters born from lower conditions. To form a young cluster of a given age, stellar mass, and mass surface density, these models need to assume a parent molecular clump that is many times denser, which is unrealistic compared to observed systems. We also show that, in these models, the initial binary properties are only slightly modified by interactions, meaning that the binary properties, e.g., at 20 Myr, are very similar to those at birth. With this study, we set up the foundation for future work, where we will investigate more realistic models of star formation compared to this instantaneous, baseline case.

An unsupervised approach for co-channel speech separation using Hilbert–Huang transform and Fuzzy C-Means clustering

In this paper we discuss an unsupervised approach for co-channel speech separation where two speakers are speaking simultaneously over same channel. We propose a two stage separation process where the initial stage is based on empirical mode decomposition (EMD) and Hilbert transform generally known as Hilbert---Huang transform. EMD decomposes the mixed signal into oscillatory functions known as intrinsic mode functions. Hilbert transform is applied to find the instantaneous amplitudes and Fuzzy C-Means clustering is applied to group the speakers at initial stage. In second stage of separation speaker groups are transformed into time---frequency domain using short time Fourier transform (STFT). Time---frequency ratio's are computed by dividing the STFT matrix of mixed speech signal and STFT matrix of stage1 recovered speech signals. Histogram of the ratios obtained can be used to estimate the ideal binary mask for each speaker. These masks are applied to the speech mixture and the underlying speakers are estimated. Masks are estimated from the speech mixture and helps in imputing the missing values after stage1 grouping of speakers. Results obtained show significant improvement in objective measures over other existing single-channel speech separation methods.

ICP: Instantaneous clustering protocol for wireless sensor networks

Wireless sensor network (WSN) is one of the mainstay technologies in Internet of Things. In WSNs, clustering is to organize scattered sensor nodes into a cluster-topology network for communications. Existing efforts on clustering intensively focus on the energy-efficiency issue. However, in mission-critical applications, a fast clustering scheme, which can not only gather sensory data immediately after deployment but also reduce the energy consumption, is more desired. In this paper, we study the clustering problem considering both time- and energy-efficiency. We propose a novel instantaneous clustering protocol (ICP) that groups sensor nodes into single-hop clusters in a parallel manner. ICP can instantaneously complete the clustering due to two key designs. First, to determine the cluster heads locally. Existing methods require a long duration on cluster head voting. To waive the voting consumption, a cluster head in ICP is locally determined by the pre-assigned probability and its present status. Second, to minimize the amount of transmissions. Parallel transmissions from different cluster heads and acknowledgments (ACKs) from multiple cluster members lead to severe time and energy consumption. On the contrary, ICP gets rid of the ACK mechanism, instead, only cluster heads contend to broadcast during a given period. This period is elaborately derived to guarantee the connectivity. Experiments on a 64-node testbed and simulations on large-scale WSNs are extensively conducted to evaluate ICP. Performance results demonstrate that ICP significantly outperforms existing clustering methods by reducing up to 55% time consumption and 89% amount of transmissions for energy-saving.

Dynamical Topology Analysis Of Vanet Based On Complex Networks Theory

Abstract A Vehicular Ad hoc NETwork (VANET) is a special subset of multi-hop Mobile Ad hoc Networks, in which the vehicles wireless interfaces can communicate with each other, as well as with fixed equipments alongside city roads or highways. Vehicular mobility dynamic characteristics, including high speed, predictable, restricted mobility pattern significantly affect the performance of routing protocols in a real VANET. Based on the existing studies, here we propose a testing network according to the preferential attachment on the degree of nodes and analyze VANET model characteristics for finding out the dynamic topology from the instantaneous degree distribution, instantaneous clustering coefficient and average path length. Analysis and simulation results demonstrate that VANET has a small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The dynamic topology analysis indicates a possible mechanism of VANET, which might be helpful in the traffic congestion, safety and management.

SPIKY: A graphical user interface for tracking spike train similarity

Modern electrophysiology is developing fast and it sometimes provides more data than available algorithms and methods can handle and process. In particular, there is an increasing demand for methods which provide the possibility to study similarity patterns of activity across many neurons. Accordingly, a wide variety of approaches to quantify the similarity (or dissimilarity) between two or more spike trains has been suggested. Recently, the ISI- and the SPIKE-distance [1,2] have been proposed as parameter-free and time-scale independent measures of spike train synchrony. The key property of both measures is that they are time-resolved since they rely on instantaneous estimates of spike train dissimilarity. This makes it possible to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. The SPIKE-distance also comes in a causal variant [2] which is defined such that the instantaneous values of dissimilarity are defined from past information only so that time-resolved spike train synchrony can be estimated in real-time. For both the regular and the real-time SPIKE-distance, there are several levels of information reduction [3]. The starting point is the most detailed representation in which one instantaneous value is obtained for each pair of spike trains. This results in a matrix of size ’number of sampled time instants’ × ’squared number of spike trains’ (i.e. #(tn)N2). By selecting a pair of spike trains one obtains a bivariate dissimilarity profile whereas the selection of a time instant yields an instantaneous matrix of pairwise spike train dissimilarities which can be used to divide the spike trains into instantaneous clusters, i.e., groups of spike trains with low intra-group and high inter-group dissimilarity. Another way to reduce the information is averaging. The spatial average over spike train pairs yields a dissimilarity profile for the selected (sub)population, whereas temporal averaging leads to a bivariate distance matrix for the selected interval or the selected trigger points. Finally, application of the remaining average results in one distance value which describes the overall level of synchrony for a group of spike trains over a given time interval. The Matlab source codes for calculating and visualizing both the ISI- and the SPIKE-distance have been made publicly available and have already been widely used in various contexts. However, the use of these codes is not very intuitive and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and accessible interface. Here we address this need and present an advanced version of the graphical user interface SPIKY [4,5]. This interactive program facilitates the application of the ISI- and the SPIKE-distance to both simulated and real data. Once given a set of spike train data (importable from many different formats), SPIKY calculates the desired measure and allows visualization of different representations such as measure profiles and pairwise dissimilarity matrices. SPIKY also includes the possibility to generate movies which are very useful in order to track the varying patterns of (dis)similarity. Finally, SPIKY has been optimized with respect to both computation speed (by using MEX-files, i.e. C-based Matlab executables) and memory demand (by taking advantage of the piecewise linear nature of the dissimilarity profiles).

SPIKY: a graphical user interface for monitoring spike train synchrony

With the growing availability of multi-unit recordings there is increasing demand for methods which provide the possibility to study similarity patterns of activity across many neurons. Accordingly, a wide variety of approaches to quantify the similarity (or dissimilarity) between two or more spike trains has been suggested. Recently, the ISI- and the SPIKE-distance [1,2] have been proposed as parameter-free and time-scale independent measures of spike train synchrony. The key property of both measures is that they are time-resolved since they rely on instantaneous estimates of spike train dissimilarity. This makes it possible to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. The SPIKE-distance also comes in a causal variant [2] which is defined such that the instantaneous values of dissimilarity are defined from past information only so that time-resolved spike train synchrony can be estimated in real-time. For both the regular and the real-time SPIKE-distance, there are several levels of information reduction [3]. The starting point is the most detailed representation in which one instantaneous value is obtained for each pair of spike trains. This results in a matrix of size 'number of sampled time instants' × 'squared number of spike trains' (i.e. #(tn)N2). By selecting a pair of spike trains one obtains a bivariate dissimilarity profile whereas the selection of a time instant yields an instantaneous matrix of pairwise spike train dissimilarities which can be used to divide the spike trains into instantaneous clusters, i.e., groups of spike trains with low intra-group and high inter-group dissimilarity. Another way to reduce the information is averaging. The spatial average over spike train pairs yields a dissimilarity profile for the selected (sub)population, whereas temporal averaging leads to a bivariate distance matrix for the selected interval or the selected trigger points. Finally, application of the remaining average results in one distance value which describes the overall level of synchrony for a group of spike trains over a given time interval. The Matlab source codes for calculating and visualizing both the ISI- and the SPIKE-distance have been made publicly available and have already been widely used in various contexts. However, the use of these codes is not very intuitive and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and accessible interface. Here we address this need and present the graphical user interface SPIKY [4,5]. This interactive program facilitates the application of the ISI- and the SPIKE-distance to both simulated and real data. SPIKY includes a spike train generator for testing purposes, as well as masks for selecting the analysis window and the neuronal subpopulation of interest. Once given a set of spike train data, it calculates the desired measure and allows visualization of all the different representations mentioned above (such as measure profiles and pairwise dissimilarity matrices). It even includes the possibility to generate movies which are very useful in order to track the varying patterns of (dis)similarity. Finally, we also have increased the high computation speed even further by transferring the most time-consuming parts of the original Matlab code to Matlab executables (MEX) with the new subroutines written in C.

Monitoring spike train synchrony

Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.

The influences of particle mass loading on mean and instantaneous particle distributions in precessing jet flows

The influence of particle mass loading on the mean and instantaneous distributions of 20μm spherical particles in the first 10 nozzle diameters downstream from a precessing jet nozzle has been investigated experimentally in an isothermal environment. Fluid flow rates were held constant, adjusting only the mass flow rate of particles, introduced through a co-annular stream around the PJ nozzle. In this way, all conditions were within the two-way coupling regime. Instantaneous planar particle distributions, measured using planar nephelometry, are reported and used to assess centreline, halfwidth and radial profiles of particle concentrations. The locations, numbers, areas and internal particle concentrations of the Instantaneous Cluster Slice (ICS) are also reported. It is found that the mass flow rate of particles has little influence to the mean distribution of particles immediately downstream from the nozzle exit plane. Further downstream, the influence of particle mass flow rate becomes more pronounced, with a narrowing and elongation of the jet. The size and concentration of ICSs increase with increasing particle mass flow rate, while the number of ICSs per image correspondingly decreases. These effects are shown to predominately correspond to the increase in total momentum flux of the particle-laden jet stream; indicating that these particle-laden jets may be estimated from only single-phase data with appropriate momentum flux ratios accounting for solid-phase effects. However, some mass loading effects are identified.

Spatial correlations of ions in nanoclusters of an aqueous solution of NaCl

The Monte Carlo method is used to calculate the radial distribution functions (RDFs) of Na+Cl−, Na+Na+, and Cl−Cl− ion pairs in water clusters that differ both in composition and size. An analysis of the RDFs shows that like-charged ions can approach one another at extremely small distances (direct ion contact) without the participation of a third ion of the opposite sign. The Na+Na+ (interionic distance 3.5 A) and Cl−Cl− (interionic distance 5.25 A) ion pairs are most likely to form in very dilute solutions. The composition of ionic associates changes with increasing concentration of ions in the solution (it is indicated by the emergence of the RDF peak at 4.25 A and its further growth with concentration and by a selective visual analysis of instantaneous cluster configurations) so that groups of three, five, etc. ions of the opposite sign bearing a net charge of 1+ or 1− appear.

A method to provide statistical measures of large-scale instantaneous particle clusters from planar images

Particle clusters are preferential accumulations of a solid, secondary phase that can be caused by turbulence. It is well known that particle clusters can influence the performance of systems employing suspension flows, such as pulverised fuel combustion systems. However, statistical analysis of clusters is limited by available methods to quantify them. In the current study, a method to identify planar slices of large-scale particle clusters from planar images of instantaneous particle distributions is presented. The method employs smoothing of instantaneous particle scatter images by a length scale, LS, to produce pseudo-scalar fields of particle distributions. The scalar fields are compared with mean (not smoothed) images to produce cluster masks that are then multiplied by the original instantaneous image to produce a map of the locations of cluster slices. The sensitivity to the smoothing length scale is assessed parametrically for its influence on the statistical measures of the following parameters characterising slices of large-scale clusters in four representative flows: the physical locations of the cluster slice centroids; the area of the cluster slice; and the number of cluster slices per image. While the results are influenced by the selected value of smoothing length scale, LS, the sensitivity is low in a physically reasonable range and the method performs well in this range for the four different flow cases. The method could be extended to provide volumetric measurements with suitable volumetric imaging systems.

The influences of jet precession on large-scale instantaneous turbulent particle clusters

An experimental investigation of the influence of jet precession on the formation of large-scale instantaneous turbulent particle clusters is reported. Instantaneous planar particle distributions in the first seven nozzle diameters downstream from a simulated pulverised fuel burner have been measured using planar nephelometry, a laser-based instantaneous concentration technique. Large-scale instantaneous particle clusters (ITPCs) are identified and quantified from these data. A systematic study is conducted to assess the influence of the ratio of the precessing jet to axial momentum streams on ITPCs. The results show that ITPCs can be modified by this momentum ratio. The average size of ITPCs reaches a maximum for cases with high precessional momentum, although excessive precessional momentum can reduce ITPC size. The particle number density per unit area inside these ITPCs reaches a maximum for an intermediate value of jet precession. The spread of ITPC centroids can be estimated from the mean jet spread of particles and therefore increases with increasing precessing jet momentum once above a certain threshold.

Impingement factor in the case of phase transformations governed by spatially correlated nucleation

The Kolmogorov-Johnson-Mehl-Avrami (KJMA) theory fails to treat nonrandom nucleation and overgrowth processes. However, the very tractability of their solution in describing experimental data caused researchers to slightly modify KJMA's differential equation so as to extend the applicability of the model. In doing this a phenomenological parameter is introduced, named as the impingement factor. Here we analyze, in depth, the limits within which the phenomenological approach is suitable for sidestepping the difficulty of nonrandom nucleation. In particular we tackled two cases: instantaneous cluster growth where cluster overgrowth prevails and the constant nucleation rate of spatially correlated nuclei according to the hard-core model. In the last part of the paper we show that Avrami's general set theory is equivalent to the statistical mechanics of rigid disks. This permits a deeper appreciation of the KJMA theory and the nonrandom nonsimultaneous kinetics.

Quasielastic lineshape of liquid Sn studied by inelastic X-ray scattering

Abstract Dynamic structure factors S(Q,ω) of liquid Sn were measured at 320 and 1000 °C in the Q range from 2 to ∼30 nm−1 using inelastic X-ray scattering (IXS). The obtained spectra clearly demonstrate the existence of longitudinal propagating modes. A positive dispersion is found in the low Q region, where the mode velocity is about 12% and 6% larger than the hydrodynamic speed of sound at 320 and 1000 °C, respectively. The quasielastic lineshape contains a Gaussian contribution near the Q position of the first maximum in S(Q), which has not yet been reported in simple liquid metals like liquid alkalis. With increasing temperature, the Gaussian contribution increases and can be observed in a wider Q range. The quasielastic lineshape is discussed by connecting with the formation of instantaneous clusters in the melt.

Mechanisms of translational and rotational energy transfer in (N2)n cluster–surface scattering

Molecular dynamics simulations were employed to investigate the dynamics of surface-induced (N2)n cluster fragmentation. The calculated translational and rotational state distributions of the monomer products of (N2)n clusters scattered off a hard surface indicate that the translational states follow a single Maxwell–Boltzmann distribution, whereas the rotational state distributions are best represented by a sum of two distinct Boltzmann distributions, in agreement with previous experimental findings obtained with a graphite surface. Analysis of the scattering dynamics provides insight into a molecular-level explanation for the differing behaviors of energy transfer to the translational and rotational modes of the monomer products. Our simulation results indicate that translational excitation of scattered products depends on the instantaneous cluster temperature at which the monomers evaporate. The obtention of two rotational distributions indicates that two rotational excitation mechanisms occur during the scattering event. The first mechanism is related to evaporation during cluster–surface impact of molecules lying at the outskirts of the cluster. The degree of rotational excitation of these molecules is shown to depend both on the degree of cluster heating during impact and on the degree of rotational hindrance imparted on a given molecule prior to evaporation. The second mechanism is related to evaporation of molecules that were caged within the interior of the cluster during surface impact. These molecules evaporate after the molecules lying at the outskirts with a degree of excitation that depends on the stabilized instantaneous cluster temperature.

Cluster birth–death processes in a vapor at equilibrium

A method is presented to analyze and observe, in molecular dynamic simulations, the statistical properties of instantaneous cluster transitions, mainly fusions and fissions for a homogeneous vapor at equilibrium. The method yields the way to obtain mean lives, branching ratios and, to some extent, transition rates as well. To the best of our knowledge branching rations in cluster decays have not been measured before (simulationally or experimentally). An application of this method to a model system provides a critical reassessment of the standard Homogeneous Nucleation Theory (HNT). Our own simulations show that transitions different from absorbing or evaporating a monomer are quite important, representing in some cases 50% of all decay events. Our method also shows unequivocally that the decay processes involving clusters classified by size alone are not Markovian.