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The kinematic scheme of a two-stream transmission is considered, which makes it possible to implement the principle of using two engines (including those using different physical principles and having different energy characteristics) and to implement the idea of design a hybrid power plant, including one adapted for operation on a driverless ship. The result of the research is the structural diagram of the transmission, the kinematic diagram of the summing gear in its composition and proposals for the implementation of the main units of the summing gear. The gearbox allows for smooth connection or disconnection of selected motors to the payload. The considered power plant combines an internal combustion engine and a traction motor. The main benefit is increased reliability through redundant motors. Improvements in efficiency are expected by ensuring consistent engine performance. The composition, the kinematic diagram, the principle of the transmission are proposed. The equations describing the kinematic and force relations in the summing gear are considered. The used method of kinematic and force analysis has been tested in the machine-building industry in relation to planetary gears as part of highly loaded transmissions of wheeled and tracked vehicles. References are given to technical solutions that make it possible to implement the proposed transmission based on the technological capabilities mastered by Russian manufacturers. The proposed scheme seems to be promising for use on ships for various purposes, including military boats, in the power plant of which a gas turbine engine is used.

The analysis of exhaled breath is drawing a high degree of interest in the diagnostics of various diseases, including lung cancer. Electronic nose (E-nose) technology is one of the perspective approaches in the field due to its relative simplicity and cost efficiency. The use of an E-nose together with pattern recognition algorithms allow 'breath-prints' to be discriminated. The aim of this study was to develop an efficient online E-nose-based lung cancer diagnostic method via exhaled breath analysis with the use of some statistical classification methods. A developed multisensory system consisting of six metal oxide chemoresistance gas sensors was employed in three temperature regimes. This study involved 118 individuals: 65 in the lung cancer group (cytologically verified) and 53 in the healthy control group. The exhaled breath samples of the volunteers were analysed using the developed E-nose system. The dataset obtained, consisting of the sensor responses, was pre-processed and split into training (70%) and test (30%) subsets. The training data was used to fit the classification models; the test data was used for the estimation of prediction possibility. Logistic regression was found to be an adequate data-processing approach. The performance of the developed method was promising for the screening purposes (sensitivity-95.0%, specificity-100.0%, accuracy-97.2%). This shows the applicability of the gas-sensitive sensor array for the exhaled breath diagnostics. Metal oxide sensors are highly sensitive, low-cost and stable, and their poor sensitivity can be enhanced by integrating them with machine learning algorithms, as can be seen in this study. All experiments were carried out with the permission of the N.N. Petrov Research Institute of Oncology ethics committee no. 15/83 dated March 15, 2017.