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Individual differences cause many differences in human behaviour, and the first source of these differences is personality. In various organisations, employees are encouraged to manage conflict through conflict management styles. The way people think can be an essential factor in their ability to conflict management. Difficult employees are individuals who constantly use problematic communication styles to express their feelings and thoughts to direct the behaviour of others. This empirical study aims to investigate the effect of thinking styles on individuals’ conflict management in dealing with difficult personalities.

To achieve the research purpose, a gamified situation was designed, and a survey was performed in laboratory settings and on an online platform. At first, participants’ reactions were measured in the simulated conflict management situation dealing with difficult personalities; subsequently, the dominant thinking style of participants was measured by the rational-experiential inventory (REI) and the cognitive reflection test. At the end, participants answered a series of demographic questions.

The collected data were then analysed by regression analysis. Based on the findings of this study, the rational thinking measured by the REI40 has a significant and positive effect on the performance of individuals in conflict management with difficult personalities in an organisational context; in other words, rational thinking leads to better performance in conflict management than experiential thinking.

The value of this article lies in the direct study of the impact of thinking styles on conflict management, which was done by focusing on difficult organisational personalities. Also, using gamification in research design is another research initiative.

This paper aims to determine the optimum arrangement of a reverse osmosis system in two methods of plug and concentrate recycling.

To compare the optimum conditions of these two methods, a seawater reverse osmosis system was considered to produce fresh water at a rate of 4,000 m3/d for Mahyarkala city, located in north of Iran, for a period of 20 years. Using genetic algorithms and two-objective optimization method, the reverse osmosis system was designed.

The results showed that exergy efficiency in optimum condition for concentrate recycling and plug methods was 82.6 and 92.4 per cent, respectively. The optimizations results showed that concentrate recycling method, despite a 36 per cent reduction in the initial cost and a 2 per cent increase in maintenance expenses, provides 6 per cent higher recovery and 19.7 per cent less permeate concentration than two-stage plug method.

Optimization parameters include feed water pressure, the rate of water return from the brine for concentrate recycling system, type of SW membrane, feedwater flow rate and numbers of elements in each pressure vessel (PV). These parameters were also compared to each other in terms of recovery (R) and freshwater unit production cost. In addition, the exergy of all elements was analyzed by selecting the optimal mode of each system.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.