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Recently, there has been an increased interest in self-healing systems. These types of systems are able to cope with failures in the environment they execute and work continuously by taking proactive actions to correct these problems. The detection of faults plays a prominent role in self-healing systems, as faults are the original causes of failures. Fault detection techniques proposed in the literature have been based on three mainstream approaches: process heartbeats, statistical analysis and machine learning. However, these approaches present limitations. Heartbeat-based techniques only detect failures, not faults. Statistical approaches generally assume linear models. Most machine learning techniques assume the data is independent and identically distributed. In order to overcome all these limitations we propose a new approach to address fault detection, which also gives insight into how process behavior changes over time in the presence of faults. Experiments show that the proposed approach achieves a twofold increase in F-measure when compared to Support Vector Machines (SVM) and Auto-Regressive Integrated Moving Average (ARIMA). [ABSTRACT FROM AUTHOR]

Abstract: Sonocatalytic degradation of Rhodamine B was investigated using titanium dioxide (TiO2) powder and titanium dioxide nanotubes (TiO2 NTs) which were synthesized using sol–gel and hydrothermal method, respectively. Structural and crystalline information for both types of catalyst were characterized using transmission electron microscope (TEM), X-ray diffractometer (XRD) and surface analyzer. Remarkable improvement in the sonocatalytic activity of TiO2 NTs was achieved due to their high specific surface area and unique pore system. Effects of reaction parameters such as pH (2–10), ultrasonic power (20–100W), ultrasonic frequency (35kHz and 130kHz) and the presence of air flow (1L/min) were also investigated. Under optimum conditions, i.e., with 8 millimolar (mM) of hydrogen peroxide (H2O2) and 1L/min of air flow at neutral pH, 85% degradation of 50mg/L Rhodamine B could be achieved in 120min by the system with 2g/L of TiO2 NTs. The sonocatalytic process was observed to behave according to a pseudo-first-order kinetics under different experimental conditions. In conclusion, sonocatalytic degradation in the presence of TiO2 NTs shows a great potential for the efficient treatment of organic dyes in wastewater. [Copyright &y& Elsevier]

Process scheduling techniques consider the current load situation to allocate computing resources. Those techniques make approximations such as the average of communication, processing, and memory access to improve the process scheduling, although processes may present different behaviors during their whole execution. They may start with high communication requirements and later just processing. By discovering how processes behave over time, we believe it is possible to improve the resource allocation. This has motivated this paper which adopts chaos theory concepts and nonlinear prediction techniques in order to model and predict process behavior. Results confirm the radial basis function technique which presents good predictions and also low processing demands show what is essential in a real distributed environment. [ABSTRACT FROM AUTHOR]

Abstract: This article presents preliminary results of a new research program for identifying predictive models for human thermoregulatory (HT) response using only an individual''s attributes and their physical property data to build the model. This program is being developed in phases and this article presents results of the first phase. This initial phase demonstrates that the proposed semi-empirical (i.e., gray-box), continuous-time, block-oriented modeling (BOM) approach [Rollins, et al. 2003. A continuous time nonlinear dynamic predictive modeling method for Hammerstein processes. Industrial and Engineering Chemistry Research 42, 861–872; Bhandari and Rollins, 2003. A continuous-time MIMO Wiener modeling method. Industrial and Engineering Chemistry Research 42, 5583–5595.] is capable of accurately predicting HT response. This ability is demonstrated using real data from literature [Hardy and Stolwijck, 1966. Partitional calorimetric studies of man during exposures to thermal transients. Journal of Applied Physiology 21(6), 1799–1806.] and computer generated data from a HT semi-theoretical model with qualitatively accurate physiological behavior [Wissler, 1963. An analysis of factors affecting temperature levels in the nude human. Temperature—its Measurement and Control in Science and Industry 3(3), 603–612; Wissler, 1964. Mathematical model of the human thermal system. Bulletin of Mathematical Biophysics 26, 147–166.]. A critical strength of the proposed gray-box BOM approach is the use of physically interpretable structures and model coefficients. This article discusses how this strength can be exploited to identify a predictive HT response model for an individual without using environmental chamber data of the individual. [Copyright &y& Elsevier]

The purpose of this work was to highlight the importance of controlling process variability for successful quality assurance (QA). We describe the method of statistical process control for characterizing and controlling a process. Traditionally, QA has been performed by comparing some important measurement (e.g., linear accelerator output) against a corresponding specification. Although useful in determining the fitness of a particular measurement, this approach does not provide information about the underlying process behavior over time. A modern view of QA is to consider the time-ordered behavior of a process. Every process displays characteristic behaviors that are independent of the specifications imposed on it. The goal of modern QA is, not only to ensure that a process is on-target, but that it is also operating with minimal variation. This is accomplished by way of a data-driven approach using process behavior charts. The development of process behavior charts, historically known as control charts, and process behavior (action) limits are described. The effect these concepts have on quality management is also discussed. [Copyright &y& Elsevier]