A survey of symbiotic organisms search algorithms and applications

Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm.

     

Prenatal tobacco use and risk of stillbirth: a case-control and bidirectional case-crossover study.

We sought to estimate the association between prenatal smoking and stillbirth in a longitudinal cohort using two study designs: a case-control study and a bidirectional case-crossover study. The analysis was conducted using the Missouri maternally linked cohort dataset from 1978 through 1997. In the case-control study, each mother contributed only one birth to the analysis. For the bidirectional crossover design, analysis was restricted to women who gave birth to at least one stillbirth, and the controls comprised all live births before and after the stillbirth. The independent association between prenatal smoking and stillbirth was computed using nonconditional (case-control design) and conditional (bidirectional case-crossover design) logistic regression. Prenatal smoking decreased from 29.7% in 1978 to 21.2% by 1997 (p<.001). The absolute risk of stillbirth was greater among smokers (7.7/1000) than nonsmokers (5.3/1000), p<.001. In the case-control design, the risk of stillbirth was 34% greater among smokers than nonsmokers (OR = 1.34, 95% CI 1.26-1.43). For each 10-unit increase in the number of cigarettes consumed per day prenatally, the likelihood of stillbirth rose by about 14% (p<.001). In the bidirectional case-crossover design, the association between stillbirth and smoking during pregnancy was confirmed, although the magnitude of the relationship was smaller (OR = 1.20, 95% CI 1.03-1.39). In conclusion, we found prenatal smoking to be a risk factor for stillbirth even after minimizing the influence of known and unknown sources of confounding as well as changes in temporal trend in prenatal smoking.