信息系统开发的并行工程方法

叙述了并行工程的概念和特点 ,并且将并行工程的原理应用于信息系统开发过程 ,提出了信息系统开发的生命周期、信息系统开发的上游工程和下游工程等概念 ,并以一具体开发实例说明这种方法的优点 .     

带有限缓冲器的多出口串行生产线无阻塞最优控制及性能分析

研究了带有限缓冲器的多出口串行生产线的无阻塞最优控制问题 ,得到了生产线的状态方程及最优控制 ,然后用极大代数上矩阵的行差单调性理论给出了生产线的性能分析.     

Telomeres and meiosis in health and disease

Telomeres are important segments of chromosomes that protect chromosome ends from nucleolytic degradation and fusion. At meiosis telomeres display an unprecedented behavior which involves their attachment and motility along the nuclear envelope. The movements become restricted to a limited nuclear sector during the so-called bouquet stage, which is widely conserved among species. Recent observations suggest that telomere clustering involves actin and/or microtubules, and is altered in the presence of impaired recombinogenic and chromosome related functions. This review aims to provide an overview of what is currently known about meiotic telomere attachment, dynamics and regulation in synaptic meiosis.     

The cold case: Are rhinoviruses perfectly adapted pathogens?

Rhinoviruses, which cause common cold, belong to the Picornaviridae family, small non-enveloped viruses (diameter 15-30 nm) containing a single-stranded RNA genome (about 7 kb). Over 100 different rhinoviral serotypes have been identified thus far, establishing rhinoviruses as the most diverse group of Picornaviridae. Based on receptor binding properties, rhinoviruses are divided into two classes: the major group binding to intracellular adhesion molecule-1 and the minor group binding to the very low density lipoprotein receptors. Interactions between virus and the receptor molecules cause a conformational change in the capsid, which is a prerequisite for viral uptake. Rhinoviruses trigger a chemokine response upon infection that may lead to exacerbation of the symptoms of common cold, i.e. asthma and inflammation. The following review aims to summarize the knowledge about rhinoviral infections and discusses therapeutical approaches against this almost perfectly adapted pathogen.     

From endoderm to pancreas: a multistep journey

The formation of the vertebrate pancreas is a complex process that typifies the basic steps of embryonic development. It involves the establishment of competence, specification, signaling from neighboring tissues, morphogenesis, and the elaboration of tissue-specific genetic networks. A full analysis of this multistep process will help us to understand classic principles of embryonic development. Furthermore, this will provide the blueprint for experimental programming of pancreas formation from embryonic stem cells in the context of diabetes cell-therapy. Although in the past decade many studies have contributed to a solid foundation for understanding pancreatogenesis, important gaps persist in our knowledge of early pancreas formation. This review will summarize the current understanding of the early mechanisms coming into play to pattern the "pre-pancreatic" region within the endoderm and, gradually, specify the pancreatic tissue.     

The Role of Survey Data in Nowcasting Euro Area GDP Growth

This paper evaluates the impact of new releases of financial, real activity and survey data on nowcasting euro area gross domestic product (GDP). We show that all three data categories positively impact on the accuracy of GDP nowcasts, whereby the effect is largest in the case of real activity data. When treating variables as if they were all published at the same time and without any time lag, financial series lose all their significance, while survey data remain an important ingredient for the nowcasting exercise. The subsequent analysis shows that the sectoral coverage of survey data, which is broader than that of timely available real activity data, as well as their information content stemming from questions focusing on agents' expectations, are the main sources of the ‘genuine’ predictive power of survey data. When the forecast period is restricted to the 2008–09 financial crisis, the main change is an enhanced forecasting role for financial data. Copyright © 2015 John Wiley & Sons, Ltd.     

Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.