Seeing mathematics teaching as a very demanding and responsible process while having in mind the importance of mathematical knowledge for students of technical faculties, this paper aims to present heuristics for student classification according to their predicted mathematical success. Over the last few decades, the process of informatization of universities has resulted in new challenges universities are faced with. Due to the widespread use of educational databases, which opens new possibilities for educational data mining and analyses, machine learning algorithms have become a very popular tool for predicting students' academic performance. The decision tree algorithm is used in this paper for the classification and prediction of students' mathematical performance and it is trained on the data collected from the educational information system. The experimental results show that the model accuracy is 72% with an error rate of 0.28. The implementation of the Decision Tree Model to predict whether a student will pass, fail or be conditional in mathematical courses is important for both teachers and students, as well as for universities. Students' performance is one of the major keys in evaluating the quality of the teaching process, but also for evaluating the overall success of the university itself. As mathematics is considered a basic and important discipline, it is clear why predicting students' mathematical achievement is crucial for all levels of university organization.

The application of circular economy principles in water supply systems has been increasingly studied recently. Concrete experiments and practical implementations in the industry are being carried out with the aim of cost savings, energy production, and material and energy recovery from wastewater treatment processes. In this context, the aim of this paper is to identify some measures that water utilities can take to enhance sustainability and reduce environmental impact. The paper provides a brief overview of the opportunities for water utilities to act on water, materials, and energy.

Energy has an effective role in economic growth and development of societies. This paper is studying the impact of climate factors on performance of solar power plant using machine learning techniques for underlying relationship among factors that impact solar energy production and for forecasting monthly energy production. In this context this work provides two machine learning methods: Artificial Neural Network (ANN) for forecasting energy production and Decision Tree (DC) useful in understanding the relationships in energy production data. Both structures have horizontal irradiation, sunlight duration, average monthly air temperature, average maximal air temperature, average minimal air temperature and average monthly wind speed as inputs parameters and the energy production as output. Results have shown that used machine learning models perform effectively, ANN predicted the energy production of the PV power plant with a correlation coefficient (R) higher than 0.97. The results can help stakeholders in determining energy policy planning in order to overcome uncertainties associated with renewable energy resources.

The current world trends and the global market require production organizations to increase the quality while reducing the costs of their products. In most cases, traditional production technologies of spiral drill bits (SD) cannot meet these expectations, as they most often fulfil only one of the set requirements. Thus, the cost of a SD produced with the rolling technology is low, but its quality is also much lower than that of the drills produced with the grinding technology whose cost is also much higher. The grooves of the SDs produced with our new technological method have advantages over the grooves produced with the rolling technology or grinding technology, and the savings in the material and grinding wheel are higher compared to the SDs produced with the grinding technology. This paper presents an analysis of the application of this new technological process for producing SD grooves.

monitoring tools condition in real time is of utmost importance in contemporary production systems. The paper presents experimentally established correlations between torque, as a reliable bearer of tool wear information, and influential parameters in drilling steel of high hardness and strength (tempered steel). On the grounds of the established correlations, regression analysis was applied to result in a mathematical model which describes torque as the function of force.