A weighted ADI scheme with variable time steps for diffusion-wave equations

Calcolo - We present a local projection stabilization for the virtual element discretization of the nonlinear convection–diffusion–reaction equation. We consider a...     

Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.     

Encrypted image classification based on multilayer extreme learning machine

Nowadays, numerous corporations (such as Google, Baidu, etc.) require an efficient and effective search algorithm to crawl out the images with queried objects from databases. Moreover, privacy protection is a significant issue such that confidential images must be encrypted in corporations. Nevertheless, decrypting and then classifying millions of encrypted images becomes a heavy burden to computation. In this paper, we proposed an encrypted image classification framework based on multi-layer extreme learning machine that is able to directly classify encrypted images without decryption. Experiments were conducted on popular handwritten digits and letters databases. Results demonstrate that the proposed framework is secure, efficient and accurate for classifying encrypted images.     

Engine ignition signal diagnosis with Wavelet Packet Transform and Multi-class Least Squares Support Vector Machines

Engine ignition pattern analysis is one of the trouble-diagnosis methods for automotive gasoline engines. Based on the waveform of the ignition pattern, the mechanic guesses what may be the potential malfunctioning parts of an engine with his/her experience and handbooks. However, this manual diagnostic method is imprecise because many ignition patterns are very similar. Therefore, a diagnosis may need many trials to identify the malfunctioning parts. Meanwhile the mechanic needs to disassemble and assemble the engine parts for verification. To tackle this problem, Wavelet Packet Transform (WPT) is firstly employed to extract the features of the ignition pattern. With the extracted features, a statistics over the frequency subbands of the pattern can then be produced, which can be used by Multi-class Least Squares Support Vector Machines (MCLS-SVM) for engine fault classification. With the newly proposed classification system, the number of diagnostic trials can be reduced. Besides, MCLS-SVM is also compared with a typical classification method, Multi-layer Perceptron (MLP). Experimental results show that MCLS-SVM produces higher diagnostic accuracy than MLP.     

Making numbers count: The early development of numerical inferences.

Two experiments examined preschool children's ability to draw inferences about numerosity from correspondences between sets. In Experiment 1, 3- and 4-year-old children made numerical inferences about a hidden set from their own counts of a corresponding visible set and also from numerical information about that set stated by the experimenter. Experiment 2 contrasted a count condition with a move condition, in which children's attention was not explicitly drawn to the numerosity of the visible set. Again, children were able to make numerical inferences as early as 3 years of age. However, differences between the 2 conditions implicate production deficiencies in young children's use of counting as a problem-solving strategy when they are not explicitly told to count. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Catalysis of H2, CO and alkane oxidation–combustion over Pt/Silica catalysts: evidence of coupling and promotion

Results are presented on the oxidative–combustion reactions of H₂, CO, methane and propane over polycrystalline-powdered silica-supported catalysts containing Pt (and possibly oxide promoters, e.g. MnO₂, etc.) and the coupling of different combustion processes and also heterogeneous–homogeneous reactions. The Pt/silica catalysts appear to mature with use. Catalysed combustions take place, as expected, at far lower temperatures and with smaller activation energies than the homogeneous reactions. The role of added or intermediate (i.e. produced by alkane partial oxidation or steam reforming) H₂ in accelerating and lowering the temperatures of catalysed alkane combustions and hence minimising NO_x emission is considered with regard to the dispersion of the Pt, as is bifurcative–hysteretic combustion in the catalysed reaction, prevalent for CO, but less certain for H₂ or alkanes. CO decelerates the catalysed combustion of hydrogen on supported Pt (and may also do this for alkane combustion). Whether the acceleration due to intermediate H₂ in alkane combustion exceeds the deceleration due to intermediate CO remains to be seen, but it may be that the water–gas shift reaction moves the advantage to H₂. Further study by in situ methods will be needed to optimise and understand this coupling so that it can be used to maximise efficient alkane-catalysed combustion with minimum NO_x production. This should also lead to higher turnover numbers (which are at present quite low for propane-catalysed combustion).     

A new family of synthetic diterpenes that regulates cytokine synthesis by inhibiting IkappaBalpha phosphorylation

The synthesis and the biological evaluation of a new family diterpenes are presented. The synthetic studies were inspired by the structural framework of acanthoic acid (1) and yielded a family of compounds that were evaluated as anti-inflammatory agents. Among them, compounds 2, 10, 12, and 16 exhibited a very low nonspecific cytotoxicity and inhibited the synthesis of TNF-alpha with greater than 65 % efficacy at low micromolar concentrations. Cytokine-specificity studies revealed that these compounds also inhibited the synthesis of the proinflammatory cytokines IL-1beta and IL-6, while inhibition of IL-1ra and IL-8 synthesis was marginal and only occurred at high concentrations. Further studies, through EMSA and Western blot analyses, indicated that these compounds decreased the extent of phosphorylation of IkappaBalpha; this suggests that they exert their anti-inflammatory profile by inhibiting NF-kappaB-mediated cytokine synthesis. These findings imply that these diterpenes represent promising leads for the development of novel anti-inflammatory agents.     

Effects of branched‐chain amino acid addition on chemical constituents in lychee wine fermented with Saccharomyces cerevisiae

This study investigated the impact of single branched‐chain amino acid addition (L‐valine, L‐leucine, L‐isoleucine) on nonvolatile and volatile constituents in lychee wine fermented with Saccharomyces cerevisiae MERIT.ferm. The individual addition of branched‐chain amino acids decreased the consumption of proline with significantly different rates. The lychee wine with added L‐leucine and L‐isoleucine had higher odour activity values (OAVs) of isoamyl alcohol, active amyl alcohol and isoamyl acetate. The lychee wine with added L‐valine had higher OAVs of isobutyric acid and isobutyl alcohol (5.55 and 2.98, respectively), which were lower in the wine with added L‐leucine (2.91 and 1.02, respectively) or L‐isoleucine (3.43 and 1.73, respectively) relative to the control wine (4.18 and 1.77, respectively), suggesting interactive effects among the amino acids. These findings demonstrated that lychee wine aroma can potentially be manipulated via the addition of selected branched‐chain amino acids.     

A High-Order Difference Scheme for the Generalized Cattaneo Equation

A high-order finite difference scheme for the fractional Cattaneo equation is investigated. The $L_1$ approximation is invoked for the time fractional part, and a compact difference scheme is applied to approximate the second-order space derivative. The stability and convergence rate are discussed in the maximum norm by the energy method. Numerical examples are provided to verify the effectiveness and accuracy of the proposed difference scheme.