Molecular dynamics simulations of diffusion of submonolayer polar liquid lubricant films on solid surfaces

A fundamental understanding of the diffusion phenomena of submonolayer polar liquid films is important for achieving reliable lubrication between moving mechanical parts separated by a nanometer-sized gap. To acquire this understanding, we conducted molecular dynamics (MD) simulations of diffusion phenomena of submonolayer polar perfluoropolyether (PFPE) Zdol films on solid surfaces. To improve the accuracy of these simulations, we developed an all-atom model that includes hydrogen-bond potential and refined atomic charges for Zdol molecules and tested it through MD simulations of spreading of step-shaped submonolayer PFPE films. Our MD simulations reproduced the experimentally observed effects of polar end groups on the diffusion speed and molecular conformation of Zdol. We then conducted MD simulations of self-diffusion of submonolayer Zdol films; these simulations demonstrated that as the thickness of the submonolayer Zdol films decreases, molecular conformation becomes flatter and the self-diffusion coefficient decreases. These changes in molecular conformation partially explain our experimental finding that the spreading of step-shaped submonolayer polar PFPE films slows down with decreasing initial thickness.     

Descent three-term conjugate gradient methods based on secant conditions for unconstrained optimization

The conjugate gradient method is an effective method for large-scale unconstrained optimization problems. Recent research has proposed conjugate gradient methods based on secant conditions to establish fast convergence of the methods. However, these methods do not always generate a descent search direction. In contrast, Y. Narushima, H. Yabe, and J.A. Ford [A three-term conjugate gradient method with sufficient descent property for unconstrained optimization, SIAM J. Optim. 21 (2011), pp. 212–230] proposed a three-term conjugate gradient method which always satisfies the sufficient descent condition. This paper makes use of both ideas to propose descent three-term conjugate gradient methods based on particular secant conditions, and then shows their global convergence properties. Finally, numerical results are given.     

Performance of SOR methods on modern vector and scalar processors

The building-cube method (BCM) is a new generation algorithm for CFD simulations. The basic idea of BCM is to simplify the algorithm in all stages of flow computation to achieve large-scale simulations. Calculation of a pressure field using the Successive Over Relaxation (SOR) method consumes most of the total execution time required for BCM. In this paper, effective implementations on modern vector and scalar processors are investigated. NEC SX-9 and Intel Nehalem-EX are the latest vector and scalar processors. Those processors have much higher peak performances than their previous-generation processors. However, their memory bandwidth improvement cannot catch up with the performance improvement of processors. This is the so-called memory wall problem. In our paper, we discuss optimization techniques for implementation of the SOR method based on architectural characteristics of these modern processors, and evaluate their effects on the sustained performances of these processors for BCM.     

Minimizing False Positives of a Decision Tree Classifier for Intrusion Detection on the Internet

Machine learning or data mining technologies are often used in network intrusion detection systems. An intrusion detection system based on machine learning utilizes a classifier to infer the current state from the observed traffic attributes. The problem with learning-based intrusion detection is that it leads to false positives and so incurs unnecessary additional operation costs. This paper investigates a method to decrease the false positives generated by an intrusion detection system that employs a decision tree as its classifier. The paper first points out that the information-gain criterion used in previous studies to select the attributes in the tree-constructing algorithm is not effective in achieving low false positive rates. Instead of the information-gain criterion, this paper proposes a new function that evaluates the goodness of an attribute by considering the significance of error types. The proposed function can successfully choose an attribute that suppresses false positives from the given attribute set and the effectiveness of using it is confirmed experimentally. This paper also examines the more trivial leaf rewriting approach to benchmark the proposed method. The comparison shows that the proposed attribute evaluation function yields better solutions than the leaf rewriting approach.

Preconditioned iterative solver on the coarsest level of a multi-grid method for high frequency time harmonic electromagnetic field analyses

A multi-grid method is one of the most powerful linear solvers for finite element electromagnetic field analysis. However, as the discretized model has recently been enlarged, a solution process for a linear system arising on the coarsest level tends to be problematic in a complete multi-grid solution process. Whereas a linear system on the coarsest level is generally solved by a direct solver, we solve it here by means of an iterative solver to reduce the memory requirements. Since a conventional preconditioning technique is not effective for such a linear system, we introduce preconditioning techniques based on Arnold, Falk, and Winther’s and on Hiptmair’s smoothers. Numerical tests show that the newly installed preconditioning technique greatly improves the convergence rate.     

Generation of uniform drops via through-hole arrays micromachined in stainless-steel plates

This study investigated the generation of oil drops using new symmetric and asymmetric through-hole-array devices made of stainless steel. The through-hole-array devices were built by piling up six stainless-steel plates, each having circular micro-holes with a diameter of 300 or 500 μm or micro-slots with a shorter line of 300 or 500 μm. Drops were generated by injecting a dispersed phase (refined soybean oil) via the through-hole array into a compartment filled with a continuous phase (Milli-Q water solution containing one of two emulsifiers). The drop detachment from symmetric and asymmetric through holes was observed in real time and analyzed. Uniform oil drops with average diameters of 1.0–4.1 mm and coefficients of variation of typically less than 6% were generated using symmetric and asymmetric through-hole-array devices. The resultant drop diameters for asymmetric through-hole arrays were significantly smaller than those for symmetric through-hole arrays. This paper also discusses experimental results regarding the effects of the microstructure, the dimensions of the through holes, and the type of emulsifier on drop generation and the resultant drop diameter.     

Straight-through microchannel devices for generating monodisperse emulsion droplets several microns in size

The authors recently proposed a promising technique for producing monodisperse emulsions using a straight-through microchannel (MC) device composed of an array of microfabricated oblong holes. This research developed new straight-through MC devices with tens of thousands of oblong channels of several microns in size on a silicon-on-insulator plate, and investigated the emulsification characteristics using the microfabricated straight-through MC devices. Monodisperse oil-in-water (O/W) and W/O emulsions with average droplet diameters of 4.4–9.8 μm and coefficients of variation of less than 6% were stably produced using surface-treated straight-through MC devices that included uniformly sized oblong channels with equivalent diameters of 1.7–5.4 μm. The droplet size of the resultant emulsions depended greatly on the size of the preceding oblong channels. The emulsification process using the straight-through MC devices developed in this research had very high apparent energy efficiencies of 47–60%, defined as (actual energy input applied to droplet generation/theoretical minimum energy input necessary for making droplets) × 100. Straight-through MC devices with numerous oblong microfluidic channels also have great potential for increasing the productivity of monodisperse fine emulsions.     

Self-organizing maps with asymmetric neighborhood function

The self-organizing map (SOM) is an unsupervised learning method as well as a type of nonlinear principal component analysis that forms a topologically ordered mapping from the high-dimensional data space to a low-dimensional representation space. It has recently found wide applications in such areas as visualization, classification, and mining of various data. However, when the data sets to be processed are very large, a copious amount of time is often required to train the map, which seems to restrict the range of putative applications. One of the major culprits for this slow ordering time is that a kind of topological defect (e.g., a kink in one dimension or a twist in two dimensions) gets created in the map during training. Once such a defect appears in the map during training, the ordered map cannot be obtained until the defect is eliminated, for which the number of iterations required is typically several times larger than in the absence of the defect. In order to overcome this weakness, we propose that an asymmetric neighborhood function be used for the SOM algorithm. Compared with the commonly used symmetric neighborhood function, we found that an asymmetric neighborhood function accelerates the ordering process of the SOM algorithm, though this asymmetry tends to distort the generated ordered map. We demonstrate that the distortion of the map can be suppressed by improving the asymmetric neighborhood function SOM algorithm. The number of learning steps required for perfect ordering in the case of the one-dimensional SOM is numerically shown to be reduced from O(N(3)) to O(N(2)) with an asymmetric neighborhood function, even when the improved algorithm is used to get the final map without distortion.