Scale-similarity model for Lagrangian velocity correlations in isotropic and stationary turbulence

A scale-similarity model for Lagrangian two-point, two-time velocity correlations LVCs in isotropic turbulence is developed from the Kolmogorov similarity hypothesis. It is a second approximation to the isocontours of LVCs, while the Smith-Hay model is only a first approximation. This model expresses the LVC by its space correlation and a dispersion velocity. We derive the analytical expression for the dispersion velocity from the Navier-Stokes equations using the quasinormality assumption. The dispersion velocity is dependent on enstrophy spectra and shown to be smaller than the sweeping velocity for the Eulerian velocity correlation. Therefore, the Lagrangian decorrelation process is slower than the Eulerian decorrelation process. The data from direct numerical simulation of isotropic turbulence support the scale-similarity model: the LVCs for different space separations collapse into a universal form when plotted against the separation axis defined by the model.

Maze model to study spatial learning and memory in freely moving monkeys

Many types of mazes have been used in cognitive brain research and data obtained from those experiments, especially those from rodents' studies, support the idea that the hippocampus is related to spatial learning and memory. But the results from non-huma

pessimistic predicate/transform model for long running business processes

Many business processes in enterprise applications are both long running and transactional in nature. However, no current transaction model can provide full transaction support for such long running business processes. This paper proposes a new transaction model, the pessimistic predicate/transform (PP/T) model, which can provide full transaction support for long running business processes. A framework was proposed on the enterprise JavaBeans platform to implement the PP/T model. The framework enables application developers to focus on the business logic, with the underlying platform providing the required transactional semantics. The development and maintenance effort are therefore greatly reduced. Simulations show that the model has a sound concurrency management ability for long running business processes.

a flexible negotiation model for an agent-based software process modelling

Our Agent-based Software Process Modelling (ASPM) approach describes a software process as a set of cooperative agents. Negotiation is the way in which the agents construct their cooperative relations, and thus the software process. Currently, most negotiation models use a fixed negotiation protocol and fixed strategies. In order to achieve the flexibility that the negotiation of the agents in ASPM requires, we propose a negotiation model NM-PA. NM-PA mainly includes a generic negotiation protocol and some rules, which possibly change in different negotiation processes. By changing the rules, the model can support multi-protocols and multi-decision-making strategies at a lower cost.

Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.

Carbon Nanotube/Silica Coaxial Nanocable as a Three-Dimensional Support for Loading Diverse Ultra-High-Density Metal Nanostructures: Facile Preparation and Use as Enhanced Materials for Electrochemical Devices and SERS

In this paper, we have reported a very simple strategy (combined sonication with sol-gel techniques) for synthesizing well-defined silica-coated carbon nanotube (CNT) coaxial nanocable without prior CNT functionalization. After functionalization with NH2 group, the CNT/silica coaxial nanocable has been employed as a three-dimensional support for loading ultra-high-density metal or hybrid nanoparticles (NPs) such as gold NPs, Au/Pt hybrid NPs, Pt hollow NPs, and Au/Ag core/shell NPs. Most importantly, it is found that the ultra-high-density Au/Pt NPs supported on coaxial nanocables (UASCN) could be used as enhanced materials for constructing electrochemical devices with high performance. Four model probe molecules (O-2, CH3OH, H2O2, and NH2NH2) have been investigated on UASCN-modified glassy carbon electrode (GCE). It was observed that the present UASCN exhibited high electrocatalytic activity toward diverse molecules and was a promising electrocatalyst for constructing electrochemical devices with high performance. For instance, the detection limit for H2O2 with a signal-to-noise ratio of 3 was found to be 0.3 mu M, which was lower than certain enzyme-based biosensors.