Analysis of an unobservable queue using arrival and departure times

Suppose one wants to assess the efficiency of a queueing system but is unable to observe directly its internal operations. This situation might arise if one works with a restricted set of historical data or because secrecy restricts access to the queueing facility. One might still be able to observe, from outside the system, the exact arrival and departure times of each customer. Using observations of arrival and departure times and knowledge of whether the service discipline is either first-come-first-served or last-come-first-served, one can exactly reconstruct the unobserved queue delays and service times of any sequence of arrivals during busy periods when the number of customers is greater than the number of servers. If the number of servers is also unknown, it too can be estimated. In this paper, we propose optimization models which determine the unknown number of servers.     

On-demand minimum cost benchmarking for intermediate dataset storage in scientific cloud workflow systems

Many scientific workflows are data intensive: large volumes of intermediate datasets are generated during their execution. Some valuable intermediate datasets need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science on clouds has become popular nowadays, more intermediate datasets in scientific cloud workflows can be stored by different storage strategies based on a pay-as-you-go model. In this paper, we build an intermediate data dependency graph (IDG) from the data provenances in scientific workflows. With the IDG, deleted intermediate datasets can be regenerated, and as such we develop a novel algorithm that can find a minimum cost storage strategy for the intermediate datasets in scientific cloud workflow systems. The strategy achieves the best trade-off of computation cost and storage cost by automatically storing the most appropriate intermediate datasets in the cloud storage. This strategy can be utilised on demand as a minimum cost benchmark for all other intermediate dataset storage strategies in the cloud. We utilise Amazon clouds’ cost model and apply the algorithm to general random as well as specific astrophysics pulsar searching scientific workflows for evaluation. The results show that benchmarking effectively demonstrates the cost effectiveness over other representative storage strategies.     

Stability and synchronization for Markovian jump neural networks with partly unknown transition probabilities

This paper addresses the problems of stability and synchronization for a class of Markovian jump neural networks with partly unknown transition probabilities. We first study the stability analysis problem for a single neural network and present a sufficient condition guaranteeing the mean square asymptotic stability. Then based on the Lyapunov functional method and the Kronecker product technique, the chaos synchronization problem of an array of coupled networks is considered. Both the stability and the synchronization conditions are delay-dependent, which are expressed in terms of linear matrix inequalities. The effectiveness of the developed methods is shown by simulation examples.     

Multi-enzyme layer-by-layer assembly for dual amplified ultrasensitive electronic detection of cancer biomarkers

The preparation and use of multi-enzyme layer-by-layer (LBL) assembled single wall carbon nanotube (SWCNT) composite labels for amplified ultrasensitive electrochemical detection of a cancer biomarker is described. The target protein, carcinoembryonic antigen (CEA), is sandwiched between an electrode surface-confined capture anti-CEA antibody and the secondary signal anti-CEA/enzyme-LBL/SWCNT bioconjugate. The dual biocatalytic signal amplification for CEA monitoring is achieved through both the numerous enzymes loaded on the CNTs and redox-recycling of the enzymatic products in the presence of the secondary enzyme and the corresponding substrate. Our novel dramatic signal amplification strategy, with a detection limit of 0.04 pg mL⁻¹, shows about 2-4 orders of magnitude improvement in sensitivity for CEA detection compared with other universal signal amplified assays, which makes our signal amplification approach hold great potential applications in detection of ultra-trace protein biomarkers.     

Soft set theory applied to p-ideals of BCI-algebras related to fuzzy points

The notions of $(\overline{\in}, \overline{\in} \vee \overline{\hbox{q}})The notions of ([`( ? )],[`( ? )] ú[`q])(\overline{\in}, \overline{\in} \vee \overline{\hbox{q}})-fuzzy p-ideals and fuzzy p-ideals with thresholds related to soft set theory are discussed. Relations between ([`( ? )],[`( ? )] ú[`q])(\overline{\in}, \overline{\in} \vee \overline{\hbox{q}})-fuzzy ideals and ([`( ? )],[`( ? )] ú[`q])(\overline{\in}, \overline{\in} \vee \overline{\hbox{q}})-fuzzy p-ideals are investigated. Characterizations of an ([`( ? )],[`( ? )] ú[`q])(\overline{\in}, \overline{\in} \vee \overline{\hbox{q}})-fuzzy p-ideal and a fuzzy p-ideal with thresholds are displayed. Implication-based fuzzy p-ideals are discussed.     

TPEMatcher: A tool for searching in parsed text corpora

Recently, due to the widespread on-line availability of syntactically annotated text corpora, some automated tools for searching in such text corpora have gained great attention. Generally, those conventional corpus search tools use a decomposition-matching-merging method based on relational predicates for matching a tree pattern query to the desired parts of text corpora. Thus, their query formulation and expressivity are often complicated due to poorly understood query formalisms, and their searching tasks may require a big computational overhead due to a large number of repeated trials of matching tree patterns. To overcome these difficulties, we present TPEMatcher, a tool for searching in parsed text corpora. TPEMatcher provides not only an efficient way of query formulation and searching but also a good query expressivity based on concise syntax and semantics of tree pattern query. We also demonstrate that TPEMatcher can be effectively used for a text mining in practice with its useful interface providing in-depth details of search results.     

闭合B样条曲线控制点的快速求解算法及应用

为了提高求解闭合B样条曲线控制点的速度,提出了一种基于专用LU分解的求解算法。根据控制点方程组系数矩阵的特点,参照追赶法的LU分解,构造了分解后的、矩阵的结构。基于这两个矩阵的结构特征设计了专用的LU分解方法,具有较少的存储空间和计算量。在此基础上,根据追赶法的原理,设计了闭合B样条曲线控制点的快速求解算法。通过数值实验和在等值线光滑中的实际应用,表明了该算法的可靠性和有效性。     

基于语义相似度的领域本体概念更新方法研究

随着信息的快速更新,知识本体经常不能满足用户的需求,提出了一个能把新关键词插入到相应本体中的领域本体更新方法。该方法用LSA(潜在语义分析)来获取关键词的语义特征,然后把LSA矩阵转换成基于Web文档的高维空间。通过比较高维空间中关键词和相应本体概念来获取关键词和概念之间的相似值,如果这个值低于某个设定值,这个关键词就插入到领域本体概念节点中。初步的试验结果表明了这个方法的有效性。     

基于Petri网的数字家庭无线射频协议研究

对数字家庭无线射频通信协议进行分解,给出协议各部分的流程。构造基于Petri网的通信协议模型,并在模型的基础上运用可达图方法分析协议Petri网模型的动态性质,从而验证协议的正确性,为进一步研究和应用该协议提供一种新的思路。