Evaluation of a multimedia online tool for teaching bronchial hygiene to physical therapy students

Background: Advances in information technology have been widely used in teaching health care professionals. The use of multimedia resources may be important for clinical learning and we are not aware of previous reports using such technology in respiratory physical therapy education. Objectives: Our approach was to evaluate a conventional bronchial hygiene techniques (BHTs) course with an interactive online environment, including multimedia resources. Methods: Previous developed audiovisual support material comprised: physiology, physiopathology and BHTs, accessible to students through the Internet in conjunction with BHTs classes. Two groups of students were compared and both attended regular classes: the on-line group (n=8) received access to online resources, while the control group (n=8) received conventional written material. Student's performance was evaluated before and after the course. Results: A preliminary test (score 0 to 10) was applied before the beginning of the course, showing that the initial knowledge of both groups was comparable [online, 6.75 (SD=0.88) vs. control, 6.125 (SD=1.35); p>0.05]. Two weeks after the end of the course, a second test showed that the online group performed significantly better than the control group [respectively, 7.75 (SD=1.28) vs. 5.93 (SD=0.72); p>0.05]. Conclusions: The use of a multimedia online resource had a positive impact on student's learning in respiratory therapy field in which instrumental and manual resources are often used and can be explored using this technology.

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.