Efficacy of NiTi rotary instruments in removing calcium hydroxide dressing residues from root canal walls

The aim of this study was to evaluate the efficacy of three rotary instrument systems (K3, Pro Taper and Twisted File) in removing calcium hydroxide residues from root canal walls. Thirty-four human mandibular incisors were instrumented with the Pro Taper System up to the F2 instrument, irrigated with 2.5% NaOCl followed by 17% EDTA, and filled with a calcium hydroxide intracanal dressing. After 7 days, the calcium hydroxide dressing was removed using the following rotary instruments: G1. - NiTi size 25, 0.06 taper, of the K3 System; G2 - NiTi F2, of the Pro Taper System; or G3 - NiTi size 25, 0.06 taper, of the Twisted File System. The teeth were longitudinally grooved on the buccal and lingual root surfaces, split along their long axis, and their apical and cervical canal thirds were evaluated by SEM (x1000). The images were scored and the data were statistically analyzed using the Kruskall Wallis test. None of the instruments removed the calcium hydroxide dressing completely, either in the apical or cervical thirds, and no significant differences were observed among the rotary instruments tested (p > 0.05).

An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.