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.     

人造麝香的危害性及其残留检测方法研究进展

综述了近年来人造麝香的危害性及其残留检测方法的研究进展。对化妆品、水、大气、土壤和药物中人造麝香的前处理方法和检测方法进行了比较,同时对此领域的发展方向进行了展望(引用文献31篇)。     

超声萃取-高效液相色谱法同时测定人造革中7种苯并三唑类紫外线吸收剂含量

建立了超声萃取-高效液相色谱法(HPLC)同时测定人造革中7种苯并三唑类紫外线吸收剂UV-P、UV-326、UV-327、UV-329、UV-350、UV-320和UV-328含量的方法。该方法以甲醇为萃取溶剂,超声萃取人造革中的苯并三唑类紫外线吸收剂,萃取液经处理后直接进行HPLC分析,外标法定量。在信噪比(S/N)=3的条件下,UV-P、UV-326、UV-327、UV-329、UV-350的检出限均为0.05mg/kg,UV-320、UV-328的检出限均为0.10 mg/kg。在3个不同加标浓度水平下,各组分的平均加标回收率为88.19%~98.32%,相对标准偏差(RSD)为0.61%~3.74%。该方法简便快捷、灵敏度高,可用于人造革中苯并三唑类紫外线吸收剂的测定。采用该方法对市售人造革样品进行测定,结果在6个样品中检出了不同浓度水平的UV-P和UV-329。     

Calibration transfer between electronic nose systems for rapid In situ measurement of pulp and paper industry emissions

Electronic nose systems when deployed in network mesh can effectively provide a low budget and onsite solution for the industrial obnoxious gaseous measurement. For accurate and identical prediction capability by all the electronic nose systems, a reliable calibration transfer model needs to be implemented in order to overcome the inherent sensor array variability. In this work, robust regression (RR) is used for calibration transfer between two electronic nose systems using a Box–Behnken (BB) design. Out of the two electronic nose systems, one was trained using industrial gas samples by four artificial neural network models, for the measurement of obnoxious odours emitted from pulp and paper industries. The emissions constitute mainly of hydrogen sulphide (H₂S), methyl mercaptan (MM), dimethyl sulphide (DMS) and dimethyl disulphide (DMDS) in different proportions. A Box–Behnken design consisting of 27 experiment sets based on synthetic gas combinations of H₂S, MM, DMS and DMDS, were conducted for calibration transfer between two identical electronic nose systems. Identical sensors on both the systems were mapped and the prediction models developed using ANN were then transferred to the second system using BB–RR methodology. The results showed successful transmission of prediction models developed for one system to other system, with the mean absolute error between the actual and predicted concentration of analytes in mg L⁻¹ after calibration transfer (on second system) being 0.076, 0.1801, 0.0329, 0.427 for DMS, DMDS, MM, H₂S respectively.     

玉米赤霉烯酮人工抗原的合成以及抗体的制备

制备了玉米赤霉烯酮人工抗原,并且制备抗血清,为进一步建立酶联免疫分析方法奠定基础.应用活泼酯法和混合酸酐法分别合成人工免疫原和人工检测原,进行动物免疫获得抗血清.结果表明,经紫外吸收、红外光谱、荧光光谱和酶联免疫方法验证人工抗原合成成功,玉米赤霉烯酮-牛血清白蛋白、玉米赤霉烯酮-卵清蛋白偶联比分别为12:1和8:1,抗...     

Convergence analysis and performance of the extended artificial physics optimization algorithm

This paper presents extended artificial physics optimization (EAPO), a population-based, stochastic, evolutionary algorithm (EA) for multidimensional search and optimization. EAPO extends the physicomimetics-based Artificial Physics Optimization (APO) algorithm by including each individual’s best fitness history. Including the history improves EAPO’s search capability compared to APO. EAPO and APO invoke a gravitational metaphor in which the force of gravity may be attractive or repulsive, the aggregate effect of which is to move individuals toward local and global optima. A proof of convergence is presented that reveals the conditions under which EAPO is guaranteed to converge. Discrete-time linear system theory is used to develop a second-order difference equation for an individual’s stochastic position vector as a function of time step. Stable solutions require eigenvalues inside the unit circle, leading to explicit convergence criteria relating the run parameters {m_i, w, G}. EAPO is tested against several benchmark functions with excellent results. The algorithm converges more quickly than APO and with better diversity.     

An improved artificial bee colony algorithm for directing orbits of chaotic systems

Artificial bee colony algorithm (ABC) is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we propose an improved ABC (IABC) by using a modified search strategy to generate a new food source in order that the exploration and exploitation can be well balanced and satisfactory optimization performances can be achieved. In addition, to enhance the global convergence, when producing the initial population, both opposition-based learning method and chaotic maps are employed. In this paper, the proposed algorithm is applied to control and synchronization of discrete chaotic systems which can be formulated as both multimodal numerical optimization problems with high dimension. Numerical simulation and comparisons with some typical existing algorithms demonstrate the effectiveness and robustness of the proposed approach.     

Assessment of the degradation of polyurethane foams after artificial and natural ageing by using pyrolysis-gas chromatography/mass spectrometry and headspace-solid phase microextraction-gas chromatography/mass spectrometry

Polyurethane foams are widely present in museum collections either as part of the artefacts, or as a material for their conservation. Unfortunately many of PU foam artefacts are in poor condition and often exhibit specific conservation issues. Their fast thermal and photochemical degradations have been the aim of previous researches. It is now accepted that hydrolysis predominates for polyester-based polyurethane PU(ES) whereas oxidation is the principal cause of degradation for polyether-based polyurethane PU(ET) variety. Only a few studies have been devoted to volatile organic compounds (VOCs) emitted by polyurethanes and, to our knowledge, none were performed on polyurethane foams by using headspace-solid phase microextraction (HS-SPME). The objective of the work described here is to assess the impact of some environmental factors (humidity, temperature and daylight) on the degradation of PU foams by evaluating their volatile fractions. We investigated morphological changes, polymerized fractions and volatile fractions of (i) one modern produced PU(ES) foam and one modern PU(ET) foam artificially aged in different conditions as well as (ii) four naturally aged foams collected from various daily life objects and selected for the representativeness of their analytical data. Characterization procedure used was based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) and non-invasive headspace solid-phase microextraction coupled with gas chromatography and mass spectrometry (HS-SPME-GC/MS). In this paper, the formation of alcohol and acid raw products for PU(ES) and glycol derivatives for PU(ET) during natural and artificial ageing is confirmed. These main products can be considered as degradation markers for PU foams. Results show that artificial and natural ageing provide similar analytical results, and confirm that the dominant degradation paths for PU(ES) and for PU(ET) are hydrolysis and photo-oxidation, respectively. Lastly, we highlight that non-invasive HS-SPME-GC/MS analysis allows to distinguish between PU(ES) and PU(ET) at any point of their degradations.     

On the use of some numerical damping methods of spurious oscillations in the case of elastic wave propagation

The use of finite element and finite difference methods of spatial and temporal discretization for solving structural dynamics problems gives rise to purely numerical errors. Among the many numerical methods used to damp out the spurious oscillations occurring in the high frequency domain, it is proposed here to analyse and compare the well-known Bulk Viscosity method, which modifies the stresses calculations and a method recently presented by Tchamwa and Wielgosz, which is based on a modification of an explicit time integration algorithm. The efficiency of both methods is evaluated in a 2-D axisymmetric compressive test.     

Artificial neural network prediction to the hot compressive deformation behavior of Al–Cu–Mg–Ag heat-resistant aluminum alloy

The behavior of the flow stress of Al-Cu-Mg-Ag heat-resistant aluminum alloys during hot compression deformation was studied by thermal simulation test. The temperature and the strain rate during hot compression were 340-500 °C, 0.001 s⁻¹ to 10 s⁻¹, respectively. Constitutive equations and an artificial neural network (ANN) model were developed for the analysis and simulation of the flow behavior of the Al-Cu-Mg-Ag alloys. The inputs of the model are temperature, strain rate and strain. The output of the model is the flow stress. Comparison between constitutive equations and ANN results shows that ANN model has a better prediction power than the constitutive equations.