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The phenomenon of self healing in concrete has been known for many years. It has been observed that some cracks in old concrete structures are lined with white crystalline material suggesting the ability of concrete to self-seal the cracks with chemical products by itself, perhaps with the aid of rainwater and carbon dioxide in air. Later, a number of researchers [1, 2] in the study of water flow through cracked concrete under a hydraulic gradient, noted a gradual reduction of permeability over time, again suggesting the ability of the cracked concrete to self-seal itself and slow the rate of water flow. The main cause of self-sealing was attributed to the formation of calcium carbonate, a result of reaction between unhydrated cement and carbon dioxide dissolved in water [1]. Thus, under limited conditions, the phenomenon of self-sealing in concrete is well established. Self-sealing is important to watertight structures and to prolonging service life of infrastructure. [ABSTRACT FROM AUTHOR]

A fuzzy-neural network for ship collision avoidance where ships are in sight of one another is proposed in this article. There are three subsets: the subset of classifying ship encounter situations and collision avoidance actions, the subset of calculating the membership functions of speed ratio, and the subset of inferring alteration magnitude and action time. The weight values of the former two subsets are obtained by self-learning from a number of samples, while those of the last subset are obtained from experience. The test results show that by the use of this network, some valuable decisions can be made. [ABSTRACT FROM AUTHOR]

In extractive summarization, a proper arrangement of extracted sentences must be found if we want to generate a logical, coherent and readable summary. This issue is special in multi-document summarization. In this paper, several existing methods each of which generate a reference relation are combined through linear combination of the resulting relations. We use 4 types of relationships between sentences (chronological relation, positional relation, topical relation and dependent relation) to build a graph model where the vertices are sentences and edges are weighed relationships of the 4 types. And then apply a variation of page rank to get the ordering of sentences for multi-document summaries. We tested our hybrid model with two automatic methods: distance to manual ordering and ROUGE score. Evaluation results show a significant improvement of the ordering over strategies losing some relations. The results also indicate that this hybrid model is robust for articles with different genre which were used on DUC2004 and DUC2005. [ABSTRACT FROM AUTHOR]

In brain-computer interfaces (BCIs), a feature selection approach using an adaptive genetic algorithm (AGA) is described in this paper. In the AGA, each individual among the population has its own crossover probability and mutation probability. The probabilities of crossover and mutation are varied depending on the fitness values of the individuals. The adaptive probabilities of crossover and mutation are propitious to maintain diversity in the population and sustain the convergence capacity of the genetic algorithms (GAs). The performance of the AGA is compared with those of the Standard GA (SGA) and the Filter method in selecting feature subset for BCIs. The results show that the classification accuracy obtained by the AGA is significantly higher than those obtained by other methods. Furthermore, the AGA has a higher convergence rate than the SGA. [ABSTRACT FROM AUTHOR]

Multi-document summarization has become a key technology in natural language processing. This paper proposes a strategy for Chinese multi-document summarization based on clustering and sentence extraction. As for clustering, we propose two heuristics to automatically detect the proper number of clusters: the first one makes full use of the summary length fixed by the user; the second is a stability method, which has been applied to other unsupervised learning problems. We also discuss a global searching method for sentence selection from the clusters. To evaluate our summarization strategy, an extrinsic evaluation method based on classification task is adopted. Experimental results on news document set show that the new strategy can significantly enhance the performance of Chinese multi-document summarization. [ABSTRACT FROM AUTHOR]

The multi-document summarizer using genetic algorithm-based sentence extraction (SBGA) regards summarization process as an optimization problem where the optimal summary is chosen among a set of summaries formed by the conjunction of the original articles sentences. To solve the NP hard optimization problem, SBGA adopts genetic algorithm, which can choose the optimal summary on global aspect. To improve the accuracy of term frequency, SBGA employs a novel method TFS, which takes word sense into account while calculating term frequency. The experiments on DUC04 data show that our strategy is effective and the ROUGE-1 score is only 0.55% lower than the best participant in DUC04. [ABSTRACT FROM AUTHOR]

In simultaneous multithreaded (SMT) processors, a larger multi-ported rename register file is indispensable for holding more intermediate results of in-flight instructions. However, larger rename register file incurs longer access delay and more power consumption, which are becoming a bottleneck in future SMT processors. To tackle these problems, we propose 2L-MuRR, the abbreviation of Multi-usable Rename Register with 2-Level renaming and allocating, which focuses on more efficient utilization of a fewer number of rename registers. Based on the fact that the effective bit-width of most operands is narrower than the full-bit width of a register entry, 2L-MuRR partitions each rename register into several fields of different widths. Either single field or field combination can hold an operand, thus making each rename register multi-usable. The simulations show that 2L-MuRR improves the efficiency of the rename register file significantly, achieving higher performance with much fewer rename registers. [ABSTRACT FROM AUTHOR]

Isolation of a full-length gene and analysis of expression profiling are fundamental and challenging in the current molecular biology. A great deal of effort is needed to detect unknown gene sequences by screening cDNA or genomic libraries by nucleic acid or protein probes. As the complete genome sequences of many organisms have been reported, this has raised the most challenging issue that how global gene expression patterns can be detected. Recently, various PCR methods have been developed for cloning of unknown genes and analysis of expression profiling (1-10). Several PCR-based strategies such as iAFLP (introduced amplified fragment length polymorphism) and TOGA (total gene-expression analysis) are currently available for global gene-expression analysis (8-10). An outline of gene cloning and expression profiling by anchored-PCR with vector primers is illustrated in Fig. 1B. In this chapter, we focus on how to use a gene library and anchored-PCR for cloning unknown gene sequences (see fig. 1 A). Friedmann et al. (1) first used PCR to screen λgt11 library with two gene-specific primers. This protocol can be effectively used to isolate a particular DNA fragment between two specific primers or to generate nucleic acid probe from cDNA libraries. The unknown sequences flanking the fragment between the two specific primers can not be amplified by this method. [ABSTRACT FROM AUTHOR]

This paper first analyzes the feedback principle of nature immune system and then the immune process is imitated by virtue of nonlinear molecular dynamics. Then the mathematic model of immune system is founded. The model implicates two important processes in immune system. One is that antibodies (Ab) and killer T cells (Tkill) rapidly respond to the change of the number of antigens (Ag). Another is that suppressor T cells (Tsup) inhibit and adjust the number of Ab and Tkill. The model manifests the good performance that immune system can respond to foreign materials rapidly and stabilize itself simultaneously. In this paper, foreign disturbances, input errors and measurement noises are regarded as Ag. The process in which creature presents immune response, produces antibodies and removes antigens is regarded as the control process of disturbances eliminating and differences adjusting. So, we designed a novel control algorithm based on immune feedback principle (IFC). The results of simulation show that the IFC algorithm can make the system respond quickly and get to steady state rapidly, and has good noise-proof feature. Its performance is superior to that of ordinary controllers. It also shows that the IFC algorithm suits for controlling the large time-delay system especially. Keywords: immune; feedback; nonlinear; control algorithm [ABSTRACT FROM AUTHOR]