Autonomic computing was introduced by IBM in 2001. Autonomic computing is a computer environment that can detect and adjust its system automatically to manage itself without the assistance of any human interaction. Autonomic computing is emerging as a significant new strategic and holistic approach to the design of complex distributed computer systems. It is inspired by the functioning of the human nervous system and is aimed at designing and building systems that are self-managing. Self-management is achieved through key aspects such as self-governing, self-adaptation, self-organization, self-optimization, self-configuration, self-diagnosis of faults, self-protection, self-healing, self-recovery, and autonomy. In this paper, we focus on the self-healing branch of the research and provide an overview of the current existing approaches. The paper is introduced by an outline of the origins of self-healing. Based on the principles of autonomic computing and self-adapting system research, we identify self-healing systems’ fundamental principles. The extracted principles support our analysis of the collected approaches.

Confidential data of people are often collected, stored, and published by different entities, such as statistical agencies or hospitals, to be analyzed and used by decision makers, politicians, researchers, etc. Thisleads to new security issues such as compromising the confidentiality of people. So we need the mechanism to protect the date sets and ensuring confidentiality of people. There are many paradigms to protect data sets containing sensitive statistical information have been proposed. The two main paradigms for data set protection are Classical and Synthetic. Recently, the possibility of combining the two paradigms, leading to a hybrid paradigm, has been considered. In this work, the securities of some synthetic and classical methods have analyzed and conclude that they suffer from a high interval disclosure risk. In this paper, the fully hybrid method is proposed to protect the confidentiality of statistical data sets with the goal of reducing its interval disclosure risk.

The sorption characteristics of cornhub powder (carbonized/uncarbonized) filled natural rubber/acrylonitrile butadiene rubber (NR/NBR) using an aromatic solvent (toluene) have been studied. The effects of cornhub powder content, particle size, nature of solvent attemperature of 35 o C were also investigated. The restriction on elastomer swelling exerted by the cornhub on the composite was investigated. Carbonized cornhub powderof 0.1µm particle size shows the lowest percentage swe lling. The molecular percentage uptake of toluene for uncarbonized cornhub was higher than the carbonized cornhub. The effect of fibre loading swelling on the swelling behavior of the composite was also investigated in toluene. The percentage swelling index andswelling coefficient of the composite were found to decreasewith increase in filler loadings and this shows anincreased hindrance exerted by the fibre.