An Efficient Soft Set-Based Approach for Conflict Analysis

Conflict analysis has been used as an important tool in economic, business, governmental and political dispute, games, management negotiations, military operations and etc. There are many mathematical formal models have been proposed to handle conflict situations and one of the most popular is rough set theory. With the ability to handle vagueness from the conflict data set, rough set theory has been successfully used. However, computational time is still an issue when determining the certainty, coverage, and strength of conflict situations. In this paper, we present an alternative approach to handle conflict situations, based on some ideas using soft set theory. The novelty of the proposed approach is that, unlike in rough set theory that uses decision rules, it is based on the concept of co-occurrence of parameters in soft set theory. We illustrate the proposed approach by means of a tutorial example of voting analysis in conflict situations. Furthermore, we elaborate the proposed approach on real world dataset of political conflict in Indonesian Parliament. We show that, the proposed approach achieves lower computational time as compared to rough set theory of up to 3.9%.     

Decolorization of Acid Red 27 and Reactive Red 2 by <Emphasis Type="Italic">Enterococcus faecalis</Emphasis> under a batch system

The objectives of this study were to investigate: (1) the capacity of Enterococcus faecalis on the decolorization of the azo dyes Acid Red 27 and Reactive Red 2; and (2) the growth characteristics of E. faecalis on those dyes. E. faecalis was able to decolorize Acid Red 27 and Reactive Red 2 effectively. High decolorization efficiency (95–100%) was achieved within 3 h of incubation for Acid Red 27, and 12 h for Reactive Red 2, at room temperature, neutral pH, static and non-aerated condition. Growth characteristics of E. faecalis on azo dyes, which were indicated by cell growth rate, biomass production, and growth yield, was worse than the control. E. faecalis grew better on Acid Red 27 rather than Reactive Red 2.     

Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

Background

Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO₂) from petroleum consumption. Limitations of the previous methods of predicting CO₂ emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO₂ emissions from petroleum consumption have motivated this research.

Methods/Findings

The OPEC CO₂ emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO₂ emissions. The proposed model predicts OPEC CO₂ emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods.

Conclusion

An accurate prediction of OPEC CO₂ emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO₂ emissions to Kyoto benchmarks—hence, reducing global warming. The policy implications are discussed in the paper.