Dielectric Behavior Of Water In [bmim] [Tf2N] Room-Temperature Ionic Liquid, Molecular Dynamic Study.

In this work we present the dielectric behavior of water with a novel flexible model that improved all three sites water models Different concentrations of the ionic liquid 1-butyl-3-methylimidazolium [bmim] bis(trifluoromethanesulfonyl)imide [Tf2N] with water was investigated. The study was performed by molecular dynamics simulations using three water models, being two non-polarizable 3-site SPC/E and SPC/e, and a novel flexible 3-site FAB/ model. Systematic thermodynamics, dynamical and dielectric properties were investigated, such as density, diffusion coefficient, heat of vaporization ∆Hvap, and surface tension at 300 K and 1 bar. We extrapolated the experimental molar fraction of the mixtures and a pattern change for all properties was observed, evidencing the phase separation previously reported by experimental data. The results also display the dielectric effect of the system on the calculated properties.

iBRAB: In silico based-designed broad-spectrum Fab against H1N1 influenza A virus

Influenza virus A is a significant agent involved in the outbreak of worldwide epidemics, causing millions of fatalities around the world by respiratory diseases and seasonal illness. Many projects had been conducting to investigate recovered infected patients for therapeutic vaccines that have broad-spectrum activity. With the aid of the computational approach in biology, the designation for a vaccine model is more accessible. We developed an in silico protocol called iBRAB to design a broad-reactive Fab on a wide range of influenza A virus. The Fab model was constructed based on sequences and structures of available broad-spectrum Abs or Fabs against a wide range of H1N1 influenza A virus. As a result, the proposed Fab model followed iBRAB has good binding affinity over 27 selected HA of different strains of H1 influenza A virus, including wild-type and mutated ones. The examination also took by computational tools to fasten the procedure. This protocol could be applied for a fast-designed therapeutic vaccine against different types of threats.

iBRAB: in silico based-designed Broad-spectrum Fab against H1N1 Influenza A Virus

Influenza virus A is a significant agent involved in the outbreak of worldwide epidemics, causing millions of fatalities around the world by respiratory diseases and seasonal illness. Many projects had been conducting to investigate recovered infected patients for therapeutic vaccines that have broad-spectrum activity. With the aid of the computational approach in biology, the designation for a vaccine model is more accessible. We developed an in silico protocol called iBRAB to design a broad-reactive Fab on a wide range of influenza A virus. The Fab model was constructed based on sequences and structure of available broad-spectrum Abs or Fabs against a wide range of H1N1 influenza A virus. As a result, the proposed Fab model followed iBRAB has good binding affinity over 27 selected HA of different strains of H1 influenza A virus, including wild-type and mutated ones. The examination also took by computational tools to fasten the procedure. This protocol could be applied for a fast designed therapeutic vaccine against different types of threats.

A Neural Network Identification Technique for a Foil-Air Bearing Under Variable Speed Conditions and Its Application to Unbalance Response Analysis

This paper proposes and studies the nonparametric system identification of a foil-air bearing (FAB). This research is motivated by two advantages: (a) it removes computational limitations by replacing the air film and foil structure equations by a displacement/force relationship and (b) it can capture complications that cannot be easily modeled, if the identification is based on empirical data. A recurrent neural network (RNN) is trained to identify the full numerical model of a FAB over a wide range of speeds. The variable-speed RNN-FAB model is then successfully validated against benchmark results in two ways: (i) by subjecting it to different input data sets and (ii) by using it in the harmonic balance (HB) solution process for the unbalance response of a rotor-bearing system. In either case, the results from the identified variable-speed RNN maintain very good correlation with the benchmark over a wide range of speeds, in contrast to an earlier identified constant-speed RNN, demonstrating the great potential of this method in the absence of self-excitation effects.

A Neural Network Identification Technique for a Foil-Air Bearing and its Application to Unbalance Response Analysis

This paper proposes and studies the non-parametric system identification of a foil-air bearing (FAB) and its application to the frequency-domain nonlinear analysis of a foil-air bearing rotor system. This research is motivated by two advantages: (i) it removes computational limitations by replacing the air film and foil structure state equations by a displacement/force relationship; (ii) if the identification is based on empirical data, it can capture complications that cannot be easily modelled. A numerical model of the FAB is identified using a recurrent neural network (RNN). The training data sets are taken from the simultaneous time domain solution of the air film, foil and rotor equations. The RNN FAB model identified at a single speed is then validated over a range of speeds in two ways: (i) by subjecting it to several sets of input-output data that are different from those used in training; (ii) by using it in the harmonic balance (HB) solution process for the unbalance response of a rotor-bearing system. In either case, the test results using the identified model show good agreement with the exact results obtained using the air film and foil equations, demonstrating the great potential of this method, in the absence of self-excitation effects.

A Swarm Intelligence Based Rescheduling Method for Semiconductor Wafer Fabrication Facilities

An effective rescheduling method takes an important role on improving the operational performance of a semiconductor wafer fabrication facility (fabs). In this paper, we propose a rescheduling method based on swarm intelligence. Firstly, we build a swarm intelligence based rescheduling model (SIRM) including an ant queen agent, multiple job ant agents and machine ant agents. Secondly, we design a rescheduling algorithm (CMRA) composed of three sub-algorithms: sub-algorithm-1 is used by an ant queen agent to transfer an existing static optimized scheduling plan into additional pheromones of job ant agents; sub-algorithm-2 and sub-algorithm-3 are used to convert scheduling related real-time information to dynamic pheromones of job ant agents and machine ant agents, respectively. Finally, a simplified semiconductor wafer fab model is used to verify and validate CMRA. The simulation results demonstrate that CMRA is superior to the original scheduling method to generate a static optimized scheduling plan with better performance on move, step and on-time operational due date rate under uncertain production environments.