A Framework for Evaluating Model-Driven Self-adaptive Software Systems

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

Testing and Modelling Market Microstructure Effects with an Application to the Dow Jones Industrial Average

It is a well accepted fact that stock returns data are often characterized by market microstructure effects, such as bid-ask spreads, liquidity ratios, turnover and asymmetric information. This is particularly relevant when dealing with high frequency data, which are often used to compute model free measures of volatility, such as realized volatility. In this paper we suggest two test statistics. The first is used to test the null hypothesis of no microstructure noise. If the null is rejected, we proceed to perform a test for the hypothesis that the microstructure noise variance is independent of the sampling frequency at which the data are recorded. We provide empirical evidence based on the Dow Jones Industrial Average, for the period 1997-2002. Our findings suggest that, while the presence of microstructure induces a severe bias when using high frequency data, such a bias grows less than linearly in the number of intraday observations.bipower variation, market microstructure, realized volatility

Role of RNA Interference (RNAi) in the Moss Physcomitrella patens

RNA interference (RNAi) is a mechanism that regulates genes by either transcriptional (TGS) or posttranscriptional gene silencing (PTGS), required for genome maintenance and proper development of an organism. Small non-coding RNAs are the key players in RNAi and have been intensively studied in eukaryotes. In plants, several classes of small RNAs with specific sizes and dedicated functions have evolved. The major classes of small RNAs include microRNAs (miRNAs) and small interfering RNAs (siRNAs), which differ in their biogenesis. miRNAs are synthesized from a short hairpin structure while siRNAs are derived from long double-stranded RNAs (dsRNA). Both miRNA and siRNAs control the expression of cognate target RNAs by binding to reverse complementary sequences mediating cleavage or translational inhibition of the target RNA. They also act on the DNA and cause epigenetic changes such as DNA methylation and histone modifications. In the last years, the analysis of plant RNAi pathways was extended to the bryophyte Physcomitrella patens, a non-flowering, non-vascular ancient land plant that diverged from the lineage of seed plants approximately 450 million years ago. Based on a number of characteristic features and its phylogenetic key position in land plant evolution P. patens emerged as a plant model species to address basic as well as applied topics in plant biology. Here we summarize the current knowledge on the role of RNAi in P. patens that shows functional overlap with RNAi pathways from seed plants, and also unique features specific to this species