Search results for " CLUSTER"
showing 10 items of 2162 documents
Use of supercritical CO2 and N2 as dissolved gases for the atomization of ethanol and water
2012
Supercritical dissolved gas atomization (SDGA) is an atomization process in which a gas at temperatures and pressures above the critical point is used as the atomizing medium. The concept of SDGA has been applied mainly using CO 2 as atomizing gas in various processes developed for the production of fine particles of pharmaceuticals, polymers, and chemical products and for the atomization of fuels. In this work, SDGA, using ethanol and water as the liquids to be atomized, has been experimentally studied. The spray characteristics, in terms of droplet size and distribution, have been investigated using a laser diffraction analyzer. Ethanol has been chosen due to the large miscibility with CO…
The gaia-eso survey: dynamical analysis of the l1688 region in ophiuchus
2016
The Gaia ESO Public Spectroscopic Survey (GES) is providing the astronomical community with high-precision measurements of many stellar parameters including radial velocities (RVs) of stars belonging to several young clusters and star-forming regions. One of the main goals of the young cluster observations is to study of their dynamical evolution and provide insight into their future, revealing if they will eventually disperse to populate the field, rather than evolve into bound open clusters. In this paper we report the analysis of the dynamical state of L1688 in the $\rho$~Ophiuchi molecular cloud using the dataset provided by the GES consortium. We performed the membership selection of t…
Cluster calibration in mass spectrometry: laser desorption/ionization studies of atomic clusters and an application in precision mass spectrometry.
2003
For accurate mass measurements and identification of atomic and molecular species precise mass calibration is mandatory. Recent studies with laser desorption/ionization and time-of-flight analysis of cluster ion production by use of fullerene and gold targets demonstrate the generation of atomic clusters for calibration purposes. Atomic ion results from the Penning trap mass spectrometer ISOLTRAP, in which a carbon cluster ion source has recently been installed, are presented as an application in the field of precision mass spectrometry.
Principle and analytical applications of resonance lonization mass spectrometry
1989
Resonance ionization mass spectrometry (RIMS) is a very sensitive analytical technique for the detection of trace elements. This method is based on the excitation and ionization of atoms with resonant laser light followed by mass analysis. It allows element and, in some cases, isotope selective ionization and is applicable to most of the elements of the periodic table. A high selectivity can be achieved by applying three step photoionization of the elements under investigation and an additional mass separation for an unambiguous isotope assignment. An effective facility for resonance ionization mass spectrometry consists of three dye lasers which are pumped by two copper vapor lasers and of…
Laser desorption/ionization cluster studies for calibration in mass spectrometry
2003
Precise mass calibration is mandatory in many fields of mass spectrometry. We have performed laser desorption/ionization cluster studies with a MALDI-TOF mass spectrometer on gold and fullerene targets to produce atomic clusters. These investigations demonstrate that clusters are ideally suited for this purpose. Pulsed N 2 -laser and Nd:YAG-laser ablation was used to produce positively as well as negatively charged clusters. Earlier observations of dianionic metal clusters are confirmed. First results from the tandem Penning trap mass spectrometer ISOLTRAP using carbon clusters as mass references show how carbon clusters can be applied to precision mass spectrometry by providing absolute ma…
Hunting active Brownian particles: Learning optimal behavior
2021
We numerically study active Brownian particles that can respond to environmental cues through a small set of actions (switching their motility and turning left or right with respect to some direction) which are motivated by recent experiments with colloidal self-propelled Janus particles. We employ reinforcement learning to find optimal mappings between the state of particles and these actions. Specifically, we first consider a predator-prey situation in which prey particles try to avoid a predator. Using as reward the squared distance from the predator, we discuss the merits of three state-action sets and show that turning away from the predator is the most successful strategy. We then rem…
Random walks in dynamic random environments and ancestry under local population regulation
2015
We consider random walks in dynamic random environments, with an environment generated by the time-reversal of a Markov process from the oriented percolation universality class. If the influence of the random medium on the walk is small in space-time regions where the medium is typical, we obtain a law of large numbers and an averaged central limit theorem for the walk via a regeneration construction under suitable coarse-graining. Such random walks occur naturally as spatial embeddings of ancestral lineages in spatial population models with local regulation. We verify that our assumptions hold for logistic branching random walks when the population density is sufficiently high.
Detection of spatial disease clusters with LISA functions.
2011
Detection of disease clusters is an important tool in epidemiology that can help to identify risk factors associated with the disease and in understanding its etiology. In this article we propose a method for the detection of spatial clusters where the locations of a set of cases and a set of controls are available. The method is based on local indicators of spatial association functions (LISA functions), particularly on the development of a local version of the product density, which is a second-order characteristic of spatial point processes. The behavior of the method is evaluated and compared with Kulldorff's spatial scan statistic by means of a simulation study. It is shown that the LI…
A fast and recursive algorithm for clustering large datasets with k-medians
2012
Clustering with fast algorithms large samples of high dimensional data is an important challenge in computational statistics. Borrowing ideas from MacQueen (1967) who introduced a sequential version of the $k$-means algorithm, a new class of recursive stochastic gradient algorithms designed for the $k$-medians loss criterion is proposed. By their recursive nature, these algorithms are very fast and are well adapted to deal with large samples of data that are allowed to arrive sequentially. It is proved that the stochastic gradient algorithm converges almost surely to the set of stationary points of the underlying loss criterion. A particular attention is paid to the averaged versions, which…
Modeling the coupled return-spread high frequency dynamics of large tick assets
2015
Large tick assets, i.e. assets where one tick movement is a significant fraction of the price and bid-ask spread is almost always equal to one tick, display a dynamics in which price changes and spread are strongly coupled. We introduce a Markov-switching modeling approach for price change, where the latent Markov process is the transition between spreads. We then use a finite Markov mixture of logit regressions on past squared returns to describe the dependence of the probability of price changes. The model can thus be seen as a Double Chain Markov Model. We show that the model describes the shape of return distribution at different time aggregations, volatility clustering, and the anomalo…