Non-interactive geometric probing: Reconstructing non-convex polygons

We address the problem of characterizing polygonal shapes that can be reconstructed from a class of scanners that have asymmetric resolution. We approach this problem using the methodology of non-interactive probing.

Laser raster scanners provide very high precision along the direction of a scan, but it is not practical to place scans very close to each other. A system capable of generating an omni-directional scan pattern can make a series of directional measurements sufficient to permit the reconstruction of a scanned polygon based on the position of edge crossings and the path of the scanning beam between edge crossings. We provide a procedure to reconstruct a polygon from such a data set, as well as a characterization of the shapes that can be reconstructed given a particular scan density. Our system applies to both concave and convex polygons, as well as to polygons containing holes.     

Assessing the predictive effectiveness of the variety seeking and reinforcement models

Prediction of customer choice behaviour has been a big challenge for marketing researchers. They have adopted various models to represent customers purchase patterns. Some researchers considered simple zero–order models. Others proposed higher–order models to represent explicitly customers tendency to seek [variety] or [reinforcement] as they make repetitive choices. Nevertheless, the question [Which model has the highest probability of representing some future data?] still prevails. The objective of this paper is to address this question. We assess the predictive effectiveness of the well–known customer choice models. In particular, we compare the predictive ability of the [dynamic attribute satiation] (DAS) model due to McAlister (Journal of Consumer Research, 91, pp. 141–150, 1982) with that of the well–known stochastic variety seeking and reinforcement behaviour models. We found that the stochastic [beta binomial] model has the best predictive effectiveness on both simulated and real purchase data. Using simulations, we also assessed the effectiveness of the stochastic models in representing various complex choice processes generated by the DAS. The beta binomial model mimicked the DAS processes the best. In this research we also propose, for the first time, a stochastic choice rule for the DAS model.     

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.     

Surface modified microarray chip and laser induced fluorescence microscopy to detect DNA cleavage

This work describes a quantitative method to detect DNA damage in the presence of Pb and Cd ions using a surface modified microarray chip and a laser induced fluorescence microscopy (LIFM). The detection was carried out by the immobilization of a single-stranded DNA oligomer, tagged with a Cy5 fluorophore on a polydimethylsiloxane (PDMS) microarray chip followed by LIFM. Sulfosuccinimidyl-4-(N-maleimidomethyl) cyclohexane-1-carboxylate (Sulfo-SMCC) was attached as a cross-linker via the formation of covalent amide bonds. Then, the single-stranded DNA oligomer containing Cy5 as a fluorophore and thiol functional groups at both terminals, was bonded to the linker by reaction with sulfhydryl group. As the DNA oligomers were reacted with metal ions of Pb and Cd, the un-cleaved DNA oligomers were quantitatively identified by monitoring Cy5 fluorescence. Cadmium showed a quenching constant of 0.84 in the Stern–Volmer plot, whereas lead gave 0.22, indicating that cadmium ions suppress fluorescence more than lead ions. When optimized, fluorescence reductions of 23% (± 2.1) for Pb and 25% (± 1.4) for Cd were observed in air and decreased to almost < 5.0% in a radical scavenger of 5 mM. The cleaved DNA was also confirmed by MALDI-TOF-MS. In result, this experimental method using a microarray chip with surface modification provided quantitative determination of DNA oligomer damage with reproducible results, significantly reduced sample volumes and analysis times.     

多波长薄层扫描分析非那西丁和咖啡因

将阵列感光的光纤光谱仪与二维电移平台结合,完成多波长薄层反射扫描定量,提供二阶张量数据输出.设计的仪器采取“弓”形二维移动扫描,探头与薄层板间距2mm,能在200ms积分时间内同时获取紫外—近红外2048个波长响应值,利用Kubelka-Munk函数积分值定量,提高了薄层扫描定量的线性度和准确度,检出范围覆盖纳克到毫克...     

Genetic Algorithm Applied to the Selection of Conditions for the Simultaneous Quantification of Three-Food Colorants Using a Hand Scanner

A low-cost and reliable method employing a hand scanner for simultaneous colorimetric quantification of food colorant mixtures including amaranth, brilliant blue, and tartrazine is presented. Compared to a spectrophotometer, a hand scanner is inexpensive, available in most work offices, and easier to operate by non-skilled users. The appropriate instrumental conditions for measuring were selected using a genetic algorithm (GA) coupled with partial least square (PLS) regression. Using the conditions selected by GA, PLS and multiple linear regression (MLR) were compared, and similar results for the two methods were obtained. Under the selected conditions for each of the colorants, artificial neural network (ANN) including three layers of nodes and a Levenberg-Marquardt learning rule was employed, which improved the results. The concentration ranges for the three colorants in the multivariate calibration models were 0.00–5.31mmolL^–1 for amaranth, 0.00–1.85mmolL^–1 for brilliant blue, and 0.00–21.57mmolL^–1 for tartrazine. The minimum estimated relative standard error percentages (RSE%) for prediction of analytes in synthetic samples, using ANN with optimized parameters, were 16.8% for amaranth, 4.8% for brilliant blue, and 5.6% for tartrazine. A number of commercial food products were analyzed satisfactorily with the proposed method.     

Applications of the luminol chemiluminescent reaction in analytical chemistry

This critical review discusses the results published between 2000 and 2005 on the development of analytical systems based on the luminol chemiluminescent and electrochemiluminescent reactions. An increasing number of non-specific detection systems based on the enhancing, inhibiting or catalysing effect of a large range of compounds have been published. Possible detected compounds and their concomitant presence in samples are discussed. Chemiluminescent and electrochemiluminescent reactions were also found to merge in biochip and microarray development as a possible substitute to the well-established but hardly quantitative fluorescent detections.     

Study on fast linear scanning for a new laser scanner

In the field of lidar system design, there is a need for laser scanners that offer fast linear scanning, are small size and have small a rotational inertia moment. Currently, laser scanners do not meet the above needs. A new laser scanner based on two amplified piezoelectric actuators is designed in this paper. The laser scanner has small size, high mechanical resonance frequencies and a small rotational inertia moment. The size of the mirror is 20 mm×15 mm. To achieve fast linear scanning performance, an open-loop controller is designed to compensate the hysteresis behavior and to restrain oscillations that are caused by the mechanical resonances of the scanner's mechanical structure. By comparing measured scanning waveforms, nonlinearities and scan line images between the uncontrolled and controlled scanner, it was found that the scanning linearity of linear scanning was improved The open-loop controlled laser scanner realizes linear scanning at 250 Hz with optical scan angle of ±12 mrad.     

Optical design for enhanced prism type fingerprint scanner image contrast with asymmetrical aspheric lens

The design of optical prism fingerprint scanner with aspheric lens is researched. We propose a modified structure for optical prism fingerprint scanner, which has an asymmetrical aspheric lens on the output plane. It is numerically shown under elliptic valley similar fingerprint model that the optical image qualities of such a scanner are better than the scanner with symmetrical aspheric lens. When the fingerprint range is 15 mm, the results show that the minimum image contrast is 0.49 for an optical prism with asymmetrical aspheric lens, much better than a symmetrical aspheric lens with 0.20. The relative illumination variation is from 0.62 to 1.00 for optical prism with asymmetrical aspheric lens, and for the symmetrical aspheric lens it is 0.35 to 1.00. We conclude that an optical prism with asymmetrical aspheric lens is better than an optical prism with symmetrical aspheric lens.     

A class imbalance-aware Relief algorithm for the classification of tumors using microarray gene expression data

DNA microarray data has been widely used in cancer research due to the significant advantage helped to successfully distinguish between tumor classes. However, typical gene expression data usually presents a high-dimensional imbalanced characteristic, which poses severe challenge for traditional machine learning methods to construct a robust classifier performing well on both the minority and majority classes. As one of the most successful feature weighting techniques, Relief is considered to particularly suit to handle high-dimensional problems. Unfortunately, almost all relief-based methods have not taken the class imbalance distribution into account. This study identifies that existing Relief-based algorithms may underestimate the features with the discernibility ability of minority classes, and ignore the distribution characteristic of minority class samples. As a result, an additional bias towards being classified into the majority classes can be introduced. To this end, a new method, named imRelief, is proposed for efficiently handling high-dimensional imbalanced gene expression data. imRelief can correct the bias towards to the majority classes, and consider the scattered distributional characteristic of minority class samples in the process of estimating feature weights. This way, imRelief has the ability to reward the features which perform well at separating the minority classes from other classes. Experiments on four microarray gene expression data sets demonstrate the effectiveness of imRelief in both feature weighting and feature subset selection applications.