The paper presents the experiments performed in the laboratory and the results obtained on friction and wear behaviour of the composite materials for the manufacture of brake shoes used for driving and towed rolling stock. The tribological research of these samples made of composite material with organic components aimed to determine the influence of certain material factors and operating regime parameters on the tribological properties of the tested samples. The brake shoes made of composite materials oneusesto replace the conventional cast iron brake shoes, to reduce the noise. The future research will focus on finding composite materials with superior properties compared to the currently used ones, and lower costs.

Due to fast technological progress in the power engineering field, the need of new information/communication technologies is more and more underlined. e-Learning has become a viable alternative to traditional teaching/learning techniques, adopted especially due to the advantages offered by the possibility of continuous training. This paper presents a Microsoft internet of things platform for a very large-scale smart power meter reading, used not only for training operative staff of the distribution network operator but also to help end users to control electrical energy that they consume. The strength of this platform for the distribution network operator is that the read data can be used for energy forecast, which is very useful for the future energy consumption optimisation. The platform can be reached through the Internet using a user name and password. A comparison between the results provided by classical teaching/learning methods and the ones achieved using this platform is presented. Keywords: Machine learning, internet of things (IoT), training.

The paper presents the results obtained after the tribology of composite materials with organic components intended for the manufacturing of brake shoes for motor and towed rolling stock. We analyzed the tribological behaviour of the samples of experimental composite material in comparison to the phosphorous cast iron frequently used for manufacturing brake shoes.

Due to fast technological progress in the power engineering field, the need for new information/communication technologies is more and more underlined. e-Learning has become a viable alternative to traditional teaching/learning techniques, adopted especially because of the advantages offered by the possibility of continuous training. This paper presents a Microsoft Internet of Things Platform for a very large scale smart power metre reading, used for training operative staff of the Distribution Network Operator, but also to help end-users to control their electrical energy consumption. The strength of this platform for the Distribution Network Operator is that the read data can be used for energy forecast, which is very useful for the future energy consumption optimisation. The platform can be reached via the Internet using a user name and password. A comparison between the results provided by classical teaching/learning methods and the ones achieved using this platform is presented. Keywords: Machine learning, internet of things (IoT), training.

The paper introduces the multiple 2 nd degree correlations between the hardness of the brake shoes used by the rolling stock and the content of carbon, phosphorus and sulphur of the phosphorous cast iron they are made of. The graphic correlations allow the determination of the variation limits for the independent parameters so as to range the values of the dependent parameters within a given domain.