Constructive model-based analysis for safety assessment

The aerospace industry still uses fault trees to perform reliability analysis. This is because fault-tree modeling and analysis (FTA) seems easier to practical engineers when compared with Markov models, even though FTA provides a weaker form of analysis. In this paper, we propose an automatic strategy for generating Markov-based models and corresponding analysis formulations, according to ARP 4761, directly from Simulink diagrams annotated with failure information. The generated Markov-based models are expressed in the formal language PRISM, and the analysis is carried out by the PRISM model checker. The strategy is compositional and based on a comprehensive set of translation rules from Simulink to PRISM. We briefly address soundness and completeness of the rules and, to illustrate the application of the strategy, we apply it to a classical avionics case study: an actuator control system.     

Bayesian bivariate generalized Lindley model for survival data with a cure fraction

The cure fraction models have been widely used to analyze survival data in which a proportion of the individuals is not susceptible to the event of interest. In this article, we introduce a bivariate model for survival data with a cure fraction based on the three-parameter generalized Lindley distribution. The joint distribution of the survival times is obtained by using copula functions. We consider three types of copula function models, the Farlie–Gumbel–Morgenstern (FGM), Clayton and Gumbel–Barnett copulas. The model is implemented under a Bayesian framework, where the parameter estimation is based on Markov Chain Monte Carlo (MCMC) techniques. To illustrate the utility of the model, we consider an application to a real data set related to an invasive cervical cancer study.     

Catalytic hydrogenation of nitrile rubber using palladium and ruthenium complexes

The catalytic hydrogenation of acrylonitrile‐butadiene copolymer (nitrile rubber, NBR) using Pd(OAc)₂ or RuCl₂(PPh₃)₃ catalysts has been investigated in order to produce a totally saturated nitrile rubber. The hydrogenation of NBR is effective with both catalysts and achieved total conversion under the appropriate reaction conditions. In the case of palladium the effects of reaction parameters such as reaction temperature, pressure, time, catalyst concentration, and NBR concentration have been investigated. Even though both ruthenium‐ and palladium‐based catalysts are effective in the production of HNBR, the former requires harsh reaction conditions and has the drawback of gel formation under high conversion, motivating the migration to RuCl₂ (PPh₃)₃ as an alternative catalyst. The degree of hydrogenation was determined by IR and NMR spectroscopy. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007     

Effect of different surface treatments on polypropylene composites reinforced with yerba mate fibers: Physical,mechanical, chemical,and morphological properties

This study presents the preparation of post-consumer polypropylene (r-PP) composites filled with 30 wt% yerba mate (YM) stick particles. To improve the fiber–matrix adhesion, three surface treatments were performed: alkaline treatment with sodium hydroxide (NaOH) and use of 3-aminopropyltriethoxysilane (APTS) and maleic anhydride graft polypropylene copolymer (PP-g-MA) as coupling agents. Mechanical properties including tensile, flexural, and impact resistance were determined, and chemical (Fourier transform infrared spectroscopy [FTIR]), physical (water absorption), and morphological analyses were performed. The main findings show that the treatments were efficient in improving the mechanical properties of the composites, with emphasis on the r-PP/YM30/APTS and r-PP/YM30/PP-g-MA composites, which proved to be superior in tensile, flexion and impact strength and absorption of water compared to the untreated composite. The morphological analysis showed a better interaction between the fiber and the polymeric matrix for the composites with YM/APTS and YM/PP-g-MA, which corroborates the results of tensile and flexural strength, as well as with the spectra of FTIR in which the chemical modification of the fibers is observed. However, the results show that these treatments are promising in obtaining composites with recycled matrix with better properties.     

CAFES: A framework for intrachip application modeling and communication architecture design

This paper describes CAFES, an extensible, open-source framework supporting several tasks related to high-level modeling and design of applications employing complex intrachip communication infrastructures. CAFES comprises several built-in models, including application, communication architecture, energy consumption and timing models. It also includes a set of generic and specific algorithms and additional supporting tools, which jointly with the cited models allow the designer to describe and evaluate applications requirements and constraints on specified communication architectures. Several examples of the use of CAFES underline the usefulness of the framework. Some of these are approached in this paper: (i) a realistic application captured at high-level that has its computation time estimated after mapping at the clock cycle level; (ii) a multi-application system that is automatically mapped to a large intrachip network with related tasks occupying contiguous areas in the chip layout; (iii) a set of mapping algorithms explored to define trade-offs between run time and energy savings for small to large intrachip communication architectures.     

Response of Corn Seedlings (Zea mays L.) to Different Concentrations of Nitrogen in Absence and Presence of Silicon

Silicon - Corn plants are highly demanding of nitrogen and the application of silicon has been studied because it minimizes stress from different natures, and for the better utilization of some...     

Mixture and non-mixture cure fraction models based on the generalized modified Weibull distribution with an application to gastric cancer data

The cure fraction models are usually used to model lifetime time data with long-term survivors. In the present article, we introduce a Bayesian analysis of the four-parameter generalized modified Weibull (GMW) distribution in presence of cure fraction, censored data and covariates. In order to include the proportion of “cured” patients, mixture and non-mixture formulation models are considered. To demonstrate the ability of using this model in the analysis of real data, we consider an application to data from patients with gastric adenocarcinoma. Inferences are obtained by using MCMC (Markov Chain Monte Carlo) methods.