Generation of droplets with different concentrations using gradient-microfluidic droplet generator

This study successfully uses the micro-mixers and flow-focusing devices, which are integrated into a gradient-microfluidic droplet generator, to generate the different sizes of the droplets with different concentrations simultaneously and applies these microcapsules for drug release. The sizes of these four types of droplet with different concentrations are uniformity with a coefficient of variation less than 5% and can be precisely controlled by adjusting the water phase flow rate and oil phase flow rate. Moreover, Ca-alginate microcapsules with different concentrations of the bovine serum albumin are used for uniform size drug release, and the Ca-alginate microcapsule size is from 60 to 105 μm in diameter. This developed microfluidic chip has the advantages of actively controlling the droplet diameter, simultaneously generating uniform-sized droplets with different concentrations, and having a simple process and a high throughput. This preparation approach for Ca-alginate microcapsules of four different concentrations will provide many potential applications for drug delivery and pharmaceutical area.     

基于机器视觉的高密度电路板缺陷检测系统

为减少高密度电路板的缺陷误报率,研究一种新型自动光学检测系统(AOI);系统采用自行研制的多色LED照明系统,利用机器视觉获取被测PCB的图像,通过图像处理软件系统快速准确地识别出各种缺陷;系统利用获取的彩色图像信息,根据各种缺陷的特征信息不同,采用OPENCV对各种缺陷的检测算法进行改进,使得系统性能有很大改进;对30块同类HDI型PCB的36300个检测点进行测试,测试结果证明,系统PCB缺陷的检出率高达99.87%,误报率只有0.32%。     

Building strength models for high-performance concrete at different ages using genetic operation trees, nonlinear regression, and neural networks

Because the behavior of HPC at early age may be rather different at late age, this study proposed to establish the strength models of HPC at different ages, and to explore the difference between these models. A large number of experimental data were used to compare accuracy of the three model building techniques, nonlinear regression analysis (NLRA), back-propagation networks (BPN), and genetic operation trees (GOT). The results showed: (1) when NLRA was employed to establish the prediction model, the approach to establish HPC strength models based on the three separate data sets was more accurate than that used to establish HPC strength models for the total data set. (2) If an explicit formula is necessary, GOT is the best choice to build concrete strength models at medium and late ages (i.e., more than 14 days), while NLRA provides greater accuracy at early ages (i.e., less than 14 days); otherwise, BPN is the best choice.     

Cascade of genetic algorithm and decision tree for cancer classification on gene expression data

Abstract: Cancer classification, through gene expression data analysis, has produced remarkable results, and has indicated that gene expression assays could significantly aid in the development of efficient cancer diagnosis and classification platforms. However, cancer classification, based on DNA array data, remains a difficult problem. The main challenge is the overwhelming number of genes relative to the number of training samples, which implies that there are a large number of irrelevant genes to be dealt with. Another challenge is from the presence of noise inherent in the data set. It makes accurate classification of data more difficult when the sample size is small. We apply genetic algorithms (GAs) with an initial solution provided by t statistics, called t‐GA, for selecting a group of relevant genes from cancer microarray data. The decision‐tree‐based cancer classifier is built on the basis of these selected genes. The performance of this approach is evaluated by comparing it to other gene selection methods using publicly available gene expression data sets. Experimental results indicate that t‐GA has the best performance among the different gene selection methods. The Z‐score figure also shows that some genes are consistently preferentially chosen by t‐GA in each data set.     

A silicone-based microfluidic chip grafted with carboxyl functionalized hyperbranched polyglycerols for selective protein capture

A poly(dimethylsiloxane) (PDMS)-based functional microfluidic device containing a charged matrix of PDMS pillar arrays grafted with hyperbranched polyglycerols (HPGs) was developed. Samples of PDMS were modified with allylamine plasma to form amine groups on the surface prior to the covalent grafting of succinimdyl ester-functionalized HPGs. The anionic functionality of the PDMS channel matrices was developed by altering the number of carboxyl groups present on the HPGs. The grafting of HPGs onto PDMS plates was investigated via contact angle measurement and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), while the grafting of the inside channel was investigated by electroosmotic flow (EOF) measurements. The charge density on grafted HPG was optimized to minimize the nonspecific protein adsorption and increase the selective capture of positively charged proteins. A proof-of-concept device was fabricated on PDMS and demonstrated that the device selectively captures positively charged protein (avidin) from a mixture of bovine serum albumin (BSA)-avidin at pH 7.4 in phosphate buffered saline (PBS). In order to increase the capture efficiency of the proteins in this PDMS-based device, pillar arrays have been fabricated within the channel. As a demonstration, the new device separated two proteins with an avidin capture efficiency of 100 ± 2.95% per 3 min from a 0.02 mg/ml protein solution (avidin:BSA wt ratio: 1:1). This new microfluidic-based device shows a great deal of promise as a tool for protein capture and analysis.     

A multiple-kernel support vector regression approach for stock market price forecasting

Support vector regression has been applied to stock market forecasting problems. However, it is usually needed to tune manually the hyperparameters of the kernel functions. Multiple-kernel learning was developed to deal with this problem, by which the kernel matrix weights and Lagrange multipliers can be simultaneously derived through semidefinite programming. However, the amount of time and space required is very demanding. We develop a two-stage multiple-kernel learning algorithm by incorporating sequential minimal optimization and the gradient projection method. By this algorithm, advantages from different hyperparameter settings can be combined and overall system performance can be improved. Besides, the user need not specify the hyperparameter settings in advance, and trial-and-error for determining appropriate hyperparameter settings can then be avoided. Experimental results, obtained by running on datasets taken from Taiwan Capitalization Weighted Stock Index, show that our method performs better than other methods.     

A study of maintenance policies for second-hand products

This paper proposes two maintenance schemes for reused products. In order to reduce the purchasing cost or conform to the concept of environmental protection, reuse is considered as one of the most efficient strategies. However, the initial quality of reused product is often inferior to the new one and then product failures will occur frequently during usage period. Therefore, two periodical preventive maintenance (PM) policies in this paper are developed to decrease the high failure rate of the second-hand products. When a second-hand product with Weibull life time distribution of known age is intended to be used for a pre-specified period of time, the optimal number of PM actions and the corresponding maintenance degree are derived such that the expected total maintenance cost is minimized. The impact of providing preventive maintenance is illustrated through numerical examples.     

Genetic algorithm-based heuristics for an open shop scheduling problem with setup,processing, and removal times separated

This report proposes a solution to the open shop scheduling problem with the objective of minimizing total job tardiness in the system. Some practical processing restrictions, such as independent setup and dependent removal times, are taken into account as well. The addressed problem is first described as a 0–1 integer programming model, and is then solved optimally. Subsequently, some hybrid genetic-based heuristics are proposed to solve the problem in an acceptable computation time. To demonstrate the adaptability of these heuristics, some performance comparisons are made with solutions provided by running either a mathematical programming model or certain classic meta-heuristics such as genetic algorithm, simulated annealing, and tabu search in various manufacturing scenarios. The experimental results show that the hybrid genetic-based heuristics perform well, especially the DGA. However, these heuristics require some more additional computations but are still acceptable.     

A commentary and correction on the article “Pansharpening by exploiting sharpness of the spatial structure”

This study clarifies the implicit potential deficiency caused by the sparse cardinality parameter k in Rong et al. (2014). In addition, k = β × W × M × N (0.9 ≤ β < 1) is suggested to avert this potential deficiency, where β is a ratio controlling the amount of sparse cardinality, W is the number of multispectral bands and M × N is the size of panchromatic image. With the choice of k suggested in this study, the low rank matrix L and sparse matrix S obtained by Go Decomposition (Zhou and Tao 2011) can be iteratively optimized and solved. Thus, instead of choosing k as W × M × N in Rong et al. (2014), the potential deficiency that L is directly obtained as an analytic solution can be averted.