In this article we argue and give evidence that the research on group recommender systems must look more carefully at the dynamics of group decision-making in order to produce technologies that will be truly beneficial for groups. We illustrate the adopted research method and the results of a user study aimed at observing and measuring the evolution of user preferences and interaction in a tourism decision-making task: finding a destination to visit together as a group. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings have on the design of interactive group recommender systems.

Summаry: Economic theory suggests that free capital flows increase the efficiency of the resource allocation, and stimulate economic growth. Foreign direct investment (FDI) is seen as a kind of cure for all economic problems in countries that do not have a sufficient level of accumulation for starting economic growth. In this paper we will investigate the impact of FDI on economic growth in Commonwealth of Independent States (Armenia, Azerbaijan, Belarus, Kazakhstan, Kyrgyzstan, Moldova, Russian Federation, Tajikistan and Ukraine) for the 2000-2015 period. Our assumption is that increase in FDI inflow will have positive impact on economic growth. The analyisis was carried out using the ARDL (Pooled Mean Group/AR Distributed Lag Models). This model is particularly convenient in a situation where all variables are stationary at different levels. The results shows strong and positive impact of FDI on economic growth.

Co-firing coal with different types of biomass is increasingly being applied in thermal power plants in Europe. The main motive for the use of biomass as the second fuel in coal-fired power plants is the reduction of CO 2 emissions, and related financial benefits in accordance with the relevant international regulations and agreements. Likewise, the application of primary measures in the combustion chamber, which also includes air staging and/or reburning, results in a significant reduction in emission of polluting components of flue gases, in particular NO x emissions. In addition to being efficient and their application to new and future thermoblocks is practically unavoidable, their application and existing conventional combustion chamber does not require significant constructional interventions and is therefore relatively inexpensive. In this work results of experimental research of co-firing coals from Middle Bosnian basin with waste woody biomass are presented. Previously formed fuel test matrix is subjected to pulverized combustion under various temperatures and various technical and technological conditions. First of all it refers to the different mass ratio of fuel components in the mixture, the overall coefficient of excess air and to the application of air staging and/or reburning. Analysis of the emissions of components of the flue gases are presented and discussed. The impact of fuel composition and process temperature on the values of the emissions of components of the flue gas is determined. Additionally, it is shown that other primary measures in the combustion chamber are resulting in more or less positive effects in terms of reducing emissions of certain components of the flue gases into the environment. Thus, for example, the emission of NO x of 989 mg/ measured in conventional combustion, with the simultaneous application of air staging and reburning is reduced to 782 mg/, or by about 21%. The effects of the primary measures applied in the combustion chamber are compared and quantified with regard to conventional combustion of coals from Middle Bosnian basin. Article History : Received: November 5 th 2017; Revised: Januari 6th 2018; Accepted: February 1 st 2018; Available online How to Cite This Article : Hodžic, N., Kazagic, A., and Metovic, S. (2018) Experimental Investigation of Co-Firing of Coal with Woody Biomass in Air Staging and Reburning. International Journal of Renewable Energy Development, 7(1), 1-6. https://doi.org/10.14710/ijred.7.1.1-6

Sleep scoring is used as a diagnostic technique in the diagnosis and treatment of sleep disorders. Automated sleep scoring is crucial, since the large volume of data should be analyzed visually by the sleep specialists which is burdensome, time-consuming tedious, subjective, and error prone. Therefore, automated sleep stage classification is a crucial step in sleep research and sleep disorder diagnosis. In this paper, a robust system, consisting of three modules, is proposed for automated classification of sleep stages from the single-channel electroencephalogram (EEG). In the first module, signals taken from Pz-Oz electrode were denoised using multiscale principal component analysis. In the second module, the most informative features are extracted using discrete wavelet transform (DWT), and then, statistical values of DWT subbands are calculated. In the third module, extracted features were fed into an ensemble classifier, which can be called as rotational support vector machine (RotSVM). The proposed classifier combines advantages of the principal component analysis and SVM to improve classification performances of the traditional SVM. The sensitivity and accuracy values across all subjects were 84.46% and 91.1%, respectively, for the five-stage sleep classification with Cohen’s kappa coefficient of 0.88. Obtained classification performance results indicate that, it is possible to have an efficient sleep monitoring system with a single-channel EEG, and can be used effectively in medical and home-care applications.