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 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.     

Enhanced sample concentration on a three-dimensional origami paper-based analytical device with non-uniform assay channel

Traditional microfluidic paper-based analytical devices (μPADs) consist of a flat straight channel printed on a paper substrate. Such devices provide a promising low-cost solution for a variety of biomedical assays. However, they have a relatively high sample consumption due to their use of external reservoirs. Moreover, in μPADs based on the ion concentration polarization (ICP) effect, controlling the cross-sectional area of the Nafion membrane relative to that of the hydrophilic channel is difficult. Accordingly, the present study utilizes an origami technique to create a μPAD with a three-dimensional (3D) structure. The μPAD features short channels and embedded reservoirs, and therefore reduces both the driving voltage requirement and the sample consumption. Moreover, the preconcentration effect is enhanced through the use of an additional hydrophilic area adjacent to the Nafion membrane. The existence of electroosmotic flow (EOF) within the proposed device is confirmed using a current-monitoring method. In addition, the occurrence of ICP is evaluated by measuring the current–voltage response of the device at external voltages ranging from 0 to 50 V. The experimental results obtained for a fluorescein sample with an initial concentration of 10^?5 M show that a 100-fold enhancement factor can be achieved given the use of a non-uniform-geometry design for the assay channel and an additional hydrophilic region with an area equal to approximately 10% of the channel cross-sectional area. Finally, a 100-fold factor can also be achieved for a fluorescein isothiocyanate sample with an initial concentration of 10^?6 M given an external driving voltage of 40 V.     

Molecular Verification of Rule-Based Systems Based on DNA Computation

Various graphic techniques have been developed to analyze structural errors in rule-based systems that utilize inference (propositional) logic rules. Four typical errors in rule-based systems are: redundancy (numerous rule sets resulting in the same conclusion); circularity (a rule leading back to itself); incompleteness (deadends or a rule set conclusion leading to unreachable goals); and inconsistency (rules conflicting with each other). This study presents a new DNA-based computing algorithm mainly based upon Adleman's DNA operations. It can be used to detect such errors. There are three phases to this molecular solution: rule-to-DNA transformation design, solution space generation, and rule verification. We first encode individual rules using relatively short DNA strands, and then generate all possible rule paths by the directed joining of such short strands to form longer strands. We then conduct the verification algorithm to detect errors. The potential of applying this proposed DNA computation algorithm to rule verification is promising given the operational time complexity of O(n*q), in which n denotes the number of fact clauses in the rule base and q is the number of rules with longest inference chain.     

Electrostatic model for an asymmetric combdrive

This paper presents an analytical solution to the electrostatic actuation of an asymmetric combdrive in out-of-plane and torsional motions. The exact solutions to force in the out-of-plane motion and the integral for torque in the torsional motion are obtained. The dependence of the peak force on the thickness of the movable fingers and the amount of overlap of the combs is given in closed form. Using our model, the shift of the natural resonant frequency due to a dc bias is analyzed. Furthermore, our solution also applies to the in-plane motion of an in-plane interdigitated combdrive. We found that an in-plane interdigitated combdrive generates a constant force within 0.1% when the minimum initial engagement length of the combs is twice the separation gap