Mineral markets, in spite of many common features with other goods markets, are distinctive. Their functioning sometimes deviates from the rules of the free market. This feature results from the specificity of acquiring the good being an object of trade. In general, changes in the supply of strategic raw materials are indicated earlier (characterized by a lengthy investment cycle from deposit reconnaissance to mining development), develop slowly, andare inelastic. Demand for common mineral raw materials often has a clear and economic character. However, mineral markets as well as markets of other goods have a common feature - the fact that both are a place where an incessant game is being played. In general, two types of strategic behaviours are distinguished: competition or cooperation. This paper recalls an existing model known as the oil market game. Based on a three-entity market of aggregate producers, an attempt has been made to model entrepreneurs' behaviour. The analysis applies n-person game theory. Game theory enables the evaluation of diverse potential coalitions forming. Possible strategies of activity coming from the prospect of cooperation (or its omission) are presented. Expected payoffs are estimated for possible alliances. Proposals for the division of the payoffs among the participants forming the coalition are also suggested.

The objective of this work is to set up a methodology that considers missing data from a connected heartbeat sensor in order to propose a good replacement methodology in the context of heart rate variability (HRV) computation. The framework is a research project, which aims to build a system that can measure stress and other factors influencing the onset and development of heart disease. The research encompasses studying existing methods, and improving them by use of experimental data from case study that describe the participant’s everyday life. We conduct a study to modelize stress from the HRV signal, which is extracted from a heart rate monitor belt connected to a smart watch. This paper describes data recording procedure and data imputation methodology. Missing data is a topic that has been discussed by several authors. The manuscript explains why we choose spline interpolation for data values imputation. We implement a random suppression data procedure and simulate removed data. After that, we implement several algorithms and choose the best one for our case study based on the mean square error.

Missing traffic data is an important issue for road administration. Although numerous ways can be found to impute them in foreign literature (inter alia, the most effective method, that is Box-Jenkins models), in Poland, still only proven and simplified methods are applied. The article presents the analyses including an assessment of the completeness of the existing traffic data and works related to the construction of SARIMA model. The study was conducted on the basis of hourly traffic volumes, derived from the continuous traffic counts stations located in the national road network in Poland (Golden River stations) from the years 2005 – 2010. As a result, the proposed model was used to impute the missing data in the form of SARIMA (1.1,1)(0,1,1)₁₆₈. The newly developed model can be used effectively to fill in the missing required days of measurement for estimating AADT by AASHTO method. In other cases, due to its accuracy and laboriousness of the process, it is not recommended.