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.     

Siamese transformer network-based similarity metric learning for cross-source remote sensing image retrieval

Neural Computing and Applications - As a fundamental technique for mining and analysis of remote sensing (RS) big data, content-based remote sensing image retrieval (CBRSIR) has received a lot of...     

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.     

Synthesis and characterization of novel blue azo‐dye derivatives containing acrylate group for dye‐based color filters

Abstract— Even though dyes have a fine resolution and good chromaticities, they are not widely used as coloring materials for color filters (CFs) due to their low thermal stability and chemical resistance. A series of azo‐dye derivatives, which consist of two cross‐linkable acrylate or methacrylate groups to improve thermal and chemical properties, have been synthesized and used to fabricate color filters. The spectral properties and chemical/thermal stabilities of the fabricated CFs were investigated by comparing dye‐based CFs, without a complicated dispersion process, but with pigment‐based CFs using dispersed pigment. Also, more properties including the development test and surface morphologies lithographic properties were studied. The synthesized azo dyes were characterized by elemental analysis, UV‐visible spectra, IR, mass, and ¹H‐NMR spectra.     

A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.     

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.