Pharmaceutical companies that manufacture, ship and supply products face difficulties in tracking their products, allowing counterfeiters to inject counterfeit drugs into the system. This situation is observed, in particular, in the Russian market of turnover of pharmaceutical products, although the problem has long been global. The ability of blockchain systems to pinpoint the origin of data makes them particularly suitable for pharmaceutical supply chain applications. The data stored in the blockchain distributed register on the identification of drugs produced by the plant, as well as records of their movements throughout the supply chain, can accurately determine the authenticity of pharmaceutical products lying on the shelves of pharmacies. The development and implementation of such a system can be a big step towards winning the exhausting fight against the easy availability of counterfeit drugs and medical products. In the first part of this work, the main characteristics and features of the functioning of blockchain systems will be studied. In the second and final part, the designed concept of the pharmaceutical turnover control system based on the blockchain technology Hyperledger Fabric using the Hyperledger Composer development environment will be investigated.

The paper considers a problem of determining the user preferred stops in a public transport recommender system. The effectiveness of using various machine learning methods to solve this problem in a system of personalized recommendations is compared, including a support vector method, a decision tree, a random forest, AdaBoost, a k-nearest neighbors algorithm, and a multi-layer perceptron. The described traditional methods of machine learning are also compared with the method proposed herein and based on an estimate calculation algorithm. The efficiency and the effectiveness of the proposed method are confirmed in the work.

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Autonomous vehicle development is one of many trends that will affect future transport demands and planning needs. Autonomous vehicles management in the context of an intelligent transportation system could significantly reduce the traffic congestion level and decrease the overall travel time in a network. In this work, we investigate a route reservation architecture to manage road traffic within an urban area. The routing architecture decomposes road segments into time and spatial slots and for every vehicle, it makes the reservation of the appropriate slots on the road segments in the selected route. This approach allows to predict the traffic in the network and to find the shortest path more precisely. We propose to use a rerouting procedure to improve the quality of the routing approach. Experimental study of the routing architecture is conducted using microscopic traffic simulation in SUMO package.