The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants’ production capacity, as well as the influence of many other factors (weather conditions, fuel costs, power plant operating costs, regulations, etc.). In this paper we try to incorporate this uncertainty in the electricity price forecasting model by applying interval numbers to express the price of electricity, with no intention of exploring influencing factors. This paper represents a hybrid model based on fuzzy C-mean clustering and the interval-valued autoregressive process for forecasting the short-term electricity price. A fuzzy C-mean algorithm was used to create interval time series to be forecasted by the interval autoregressive process. In this way, the efficiency of forecasting is improved because we predict the interval, not the crisp value where the price will be. This approach increases the flexibility of the forecasting model.

The main aim of a coal deposit model is to provide an effective basis for mine production planning. The most applied approach is related to block modeling as a reasonable global representation of the coal deposit. By selection of adequate block size, deposits can be well represented. A block has a location in XYZ space and is characterized by adequate attributes obtained from drill holes data. From a technological point of view, i.e., a thermal power plant’s requirements, heating value, sulfur and ash content are the most important attributes of coal. Distribution of attributes’ values within a coal deposit can vary significantly over space and within each block as well. To decrease the uncertainty of attributes’ values within blocks the concept of fuzzy triangular numbers is applied. Production planning in such an environment is a very hard task, especially in the presence of requirements. Such requirements are considered as target values while the values of block attributes are the actual values. To make production planning easier we have developed a coal deposit model based on clustering the relative closeness of actual values to the target values. The relative closeness is obtained by the TOPSIS method while technological clusters are formed by fuzzy C-mean clustering. Coal deposits are thus represented by multi-attribute technological mining cuts.

As the civil industry, and especially the concrete industry, constantly advance and modernize, the usage of sustainable construction materials is becoming increasingly important. These materials represent one of the most significant components in the concept of the sustainable development of every company and industry. Underground mining, as a part of mining industry, largely applies sustainable materials to perform some of its technological operations. The optimal selection of sustainable construction materials is one of the key tasks for underground mining engineers. This paper proposes a novel integrated CRIFFT-SPHERA decision-making model for choosing the most suitable concrete mixture for supporting and backfilling activities in an underground mine. A total of ten concrete mixtures forms a set of alternatives which are evaluated with respect to four criteria. To validate the proposed MCDM model, an extensive numerical calculation procedure as well as comparative analysis are conducted through a hypothetical example. The obtained results confirm the extremely high degree of stability and reliability of the developed model. Also, the results are checked using sensitivity analysis based on changes in the values of the weight coefficients. In addition, the effectiveness of the developed model is verified through real-world problems where its application for solving these problems in various industries is accepted. This paper provides valuable insights for engineers dealing with problem of the construction of different concrete forms and structures, especially in underground areas, with fly ash as an element belonging to a group of sustainable construction materials.

Forecasting the operational efficiency of an existing underground mine plays an important role in strategic planning of production. Degree of Operating Leverage (DOL) is used to express the operational efficiency of production. The forecasting model should be able to involve common time horizon, taking the characteristics of the input variables that directly affect the value of DOL. Changes in the magnitude of any input variable change the value of DOL. To establish the relationship describing the way of changing we applied multivariable grey modeling. Established time sequence multivariable response formula is also used to forecast the future values of operating leverage. Operational efficiency of production is often associated with diverse sources of uncertainties. Incorporation of these uncertainties into multivariable forecasting model enables mining company to survive in today’s competitive environment. Simulation of mean reversion process and geometric Brownian motion is used to describe the stochastic diffusion nature of metal price, as a key element of revenues, and production costs, respectively. By simulating a forecasting model, we imitate its action in order to measure its response to different inputs. The final result of simulation process is the expected value of DOL for every year of defined time horizon.

The professional development of teachers is carried out for the sake of continuous progress in the level of their professional, pedagogical and technological competencies. In contemporary conditions, it is considered that technological competencies of teachers are their capability to prepare and realize teaching in an online environment, which can be an addition to or an option of teaching in the classroom. The School of electrical and computer engineering in the Academy of technical and art applied studies in Belgrade is one of the partner institutions of the Erasmus+ program „Professional Development of Vocational Education Teachers in Russia and Serbia with European Practices Pro-VET“ (2018-2022) with the goal to develop the training program „Online course design and tutoring“. In this paper is a short overview of the above mentioned training program, which was developed (2018-2019), redesigned based on results of alfa and beta testing (2020-2021) and realized twice (2022). In the paper is a brief presentation of the following: an overview of the training program, methods used to motivate teachers to enter the program, work on it and successfully complete it, teachers’ achieved success in the program and their grades for the quality of the training program.

Computing power rises predominantly by increasing the number of cores in modern processors and the number of processors in cluster and cloud architectures. Along with increasing processing power, high-performance computing requirements also rise. The majority of the computing infrastructure includes control-flow processors that are based on the von Neumann paradigm. On the contrary, the principle of dataflow architectures is based on the data flowing through the already configured hardware. Recent research has proposed hybrid architectures, where both control-flow and dataflow hardware would exist on the same chip die. This article proposes a new hybrid control-flow and dataflow architecture where the control-flow hardware resembles modern graphical cards with thousands of cores and each GPU core has a reasonable amount of data-flow hardware. In this way, the advantages of dataflow architecture are exploited, including faster processing of high-performance computing algorithms and lower power consumption, while the conventional problem of communicating between control-flow and dataflow architectures is minimized. The proposed architecture is tested by analyzing the conjugate gradient method executed on both control-flow and dataflow hardware. The execution of the algorithm is divided onto GPU cores, and the execution of repeated instructions on each GPU core is delegated to the assigned dataflow hardware. The results indicate that it is possible to accelerate the execution of algorithms using the proposed architecture.