Two-dimensional Kerr-Fourier imaging of translucent phantoms in thick turbid media

Translucent scattering phantoms hidden inside a 5.5-cm-thick Intralipid solution were imaged as a function of phantom scattering coefficients by the use of a picosecond time- and space-gated Kerr-Fourier imaging system. A 2-mm-thick translucent phantom with a 0.1% concentration (scattering coefficient) difference from the 55-mm-thick surrounding scattering host can be distinguished at a signal level of ~10(-10) of the incidence illumination intensity.     

Free energy perturbation approach for the rational engineering of the antibody for human hepatitis B virus

HzKR127 is the humanized monoclonal antibody effective for the neutralization of human hepatitis B virus. By means of the free energy perturbation (FEP) calculations based on molecular dynamic (MD) simulations, we examine the mutation-induced variations in the energetic and structural features associated with the interactions between HzKR127 and its antigen. N58A, Y96A, D97A, and D97A/Y102A mutants of HzKR127 are taken in account in this study for which the experimental data for relative efficacies with respect to the wild-type antibody are available. The results of the present MD-FEP simulation studies show that in order to enhance the affinity for the antigen, the engineering of HzKR127 should be made in such a way as to promote the dynamic stability of the overall protein conformation and that of the translational motion of the antigen in the antibody-antigen complex. The relative binding free energies of the four mutant antibodies obtained from MD-FEP calculations compare pretty well with the experimental mutagenesis data with the associated squared correlation coefficient of 0.96. This indicates that MD-FEP calculations may serve as a useful computational tool for rational antibody engineering. Discussed in detail are the differences in the structural features of antibody-antigen interactions between the wild-type and the mutant antibodies that are responsible for the change in binding affinities for the antigen.     

A fuzzy-based interleaved multi-hop authentication scheme in wireless sensor networks

In sensor networks, a compromised node can either generate fabricated reports with false votes or inject false votes into real reports, which causes severe damage such as false alarms, energy drain and information loss. An interleaved hop-by-hop authentication (IHA) scheme addresses the former attack by detecting and filtering false reports in a deterministic and hop-by-hop fashion. Unfortunately, in IHA, all en-route nodes must join to verify reports while only a few are necessary to the authentication procedure. In this paper, we propose a fuzzy-based interleaved multi-hop authentication scheme based on IHA. In our scheme, the fuzzy logic system only selects some nodes for verification based on the network characteristics. Moreover, we apply a voting method and a hash-based key assignment mechanism to improve network security. Through performance evaluation, the proposed scheme is found to save up to 13% of the energy consumption and to provide more network protection compared to IHA.     

Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.     

A general formulation of monotonically convergent algorithms in the control of quantum dynamics beyond the linear dipole interaction

This paper presents a general method to formulate monotonically convergent algorithms for identifying optimal control fields to manipulate quantum dynamics phenomena beyond the linear dipole interaction. The method, facilitated by a field-dependent dipole moment operator, is based on an integral equation of the first kind arising from the Heisenberg equation of motion for tracking a time-dependent, dynamical invariant observable associated with a reference control field.     

A synopsis on node compromise detection in wireless sensor networks using sequential analysis (Invited Review Article)

By exploiting the unattended nature of the wireless sensor networks, an attacker can physically capture and compromise sensor nodes and then launch a variety of attacks. He can additionally create many replicas of a few compromised nodes and spread these replicas over the network, thus launching further attacks with their help. In order to minimize the damage incurred by compromised and replicated nodes, it is very important to detect such malicious nodes as quickly as possible. In this review article, we synthesize our previous works on node compromise detection in sensor networks while providing the extended analysis in terms of performance comparison to the related work. More specifically, we use the methodology of the sequential analysis to detect static and mobile compromised nodes, as well as mobile replicated nodes in sensor networks. With the help of analytical and simulation results, we also demonstrate that our schemes provide robust and efficient node compromise detection capability.