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     

A Piecewise Grey System Model for Study the Effects of Anthropogenic Activities on Karst Hydrological Processes

Sustainable groundwater development must rely on a good understanding of hydrological processes, especially under effects of anthropogenic activities. This paper develops a piecewise analysis based on grey system model for study effects of anthropogenic activities on hydrological processes. The time series of precipitation and spring discharge were segmented into three time periods depending on whether variations are due to climate variation: the predevelopment stage, the transition stage, and the new equilibrium stage. Then we modeled hydrological process of the predevelopment stage and new equilibrium stage by the grey system model. By comparing the model results, we can quantify the effects of human activities on hydrological processes. We applied the model to Liulin Springs China. The results indicated that the hydraulic response time of the spring discharge to precipitation from the predevelopment stage to the new equilibrium stage, ranges from less than 1 year to up to 4 years. The results revealed that human activities slow the groundwater flow. GM (1,N) models of Liulin Springs discharge showed that the driving coefficients of precipitation to the spring discharge decreased from 0.012272 in the predevelopment stage to 0.007753 in the new equilibrium stage, which means that groundwater recharge ability has reduced about 36.82 %. Human activities in Liulin Springs have drastically changed the groundwater system. Piecewise grey system model is a robust method for hydrological process simulation.     

Effects of the heterogeneity of game complexity and user population in learning performance of business simulation games

Previous studies on business simulation games (BSGs) have concluded that improved performance may not be the primary benefit of using BSGs, due to mixed results between student performance and perceptions. Two relevant and insightful issues attract our attention, namely, the impacts of the heterogeneous student population and the different complexity levels of BSG software. To address these issues, the present study aims to understand the relationship between student profile/characteristics and performance in the classroom with BSG-facilitated learning. An in-depth case study is conducted on a general college course designed to teach three different complexities of BSGs to students enrolled in different majors. Four student profile factors are individually tested for differences in performance scores as evaluated by the teacher. Additionally, the influences of 11 student characteristics are assessed with regard their self-reported perceived learning performances. Regression analysis and ANOVA are used to investigate the impacts of heterogeneous users and game complexity on student performance. Based on the regression analyses of the data collected from 43 respondents who participated in the general course, the study concludes that knowledge and skill may influence the heterogeneous student population; moreover, student participation and tacit learning preference improve performance, and students with an auditory learning preference or high learning motivation may not perform well in classroom BSG learning. However, the low value of adjusted R square implies that more dimensions or variables are needed to increase the explaining power of the performance scores in the regression analyses. In contrast, heterogeneous BSG software with different complexity levels present different results. The current research contributes practical and incremental knowledge on the complexity of heterogeneous BSG software on performance scores and the perceived learning performance of heterogeneous student populations. With the research limitations acknowledged, a series of suggestions for teachers pertaining to appropriate applications of BSGs in classes is offered as well as recommendations to BSG providers. Nevertheless, in-depth analyses are required, preferably with larger student population samples, to further explore the insignificant relationship between student perceptions and attitude under nonlinear extended complexity.     

Uniform parallel-machine scheduling to minimize makespan with position-based learning curves

Scheduling with learning effects has become a popular topic in the past decade; however, most of the research focuses on single-machine problems. In many situations, there are machines in parallel and the skills of workers might be different due to their individual experience. In this paper, we study a uniform parallel machine problem in which the objective is to jointly find an optimal assignment of operators to machines and an optimal schedule to minimize the makespan. Two heuristic algorithms are proposed and computational experiments are conducted to evaluate their performance.     

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.     

Microstructural development and electrical properties of lead zirconate titanate composite films

Dihydroxyalcohols (DIOL route) and 2-methoxyethanol (MOE route) are two of the most commonly used solvents in preparing sol-gel lead zirconate titanate (PZT) films. The employment of the PT(MOE) film as a buffer layer for PZT(DIOL) films not only offers advantages of more precise film thickness and better adhesion to substrate, but also reduces the crystallization temperature, improves the surface smoothness, and increases the grain size of the PZT(DIOL)/PT(MOE) composite films. The PT (MOE) buffer layer also improves electrical properties, such as polarization-electric field behavior, fatigue endurance, and leakage behavior, of PZT films.