No abstract

To create an autonomously moving vessel, it is necessary to know exactly how to determine the current coordinates of the vessel in the selected coordinate system, determine the actual trajectory of the vessel, estimate the motion trend to predict the current coordinates, and calculate the course correction to return to the line of the specified path. The navigational and hydrographic conditions of navigation on each section of the route determine the requirements for the accuracy of observations and the time spent on locating the vessel. The problem of predicting the trajectory of the vessel's motion in automatic mode is especially important for river vessels or river-sea vessels, predicting the trajectory of the route sections during the maneuvering of the vessel. At the moment, one of the most accurate ways of determining the coordinates of the vessel is by reading the satellite signal. However, when a vessel is near hydraulic structures, problems may arise connected with obtaining a satellite signal due to interference and, therefore, the error in measuring the coordinates of the vessel increases. The likelihood of collisions and various kinds of incidents increases. In such cases, it is possible to correct the trajectory of the movement using an autonomous navigation system. In this work, opportunities of the possible application of artificial neural networks to create such a corrective system using only the coordinates of the ship's position are discussed. It was found that this is possible on sections of the route where the ship does not maneuver.

The mathematical model of malignant tumor, which represents itself an initial boundary task for system of differential equations in partial derivatives, has been developed. In model there are 3 types of cells, which interact between each other, it is a dividing, normal and dead cells. An inhibiting influence of cells at each other is considered in that way that the growth of continuously dividing cells is accompanied by destruction of normal cells and emergence of dead cells. Analysis of stability of stationary decisions has been done. Evaluation of velocity of distribution tumor on a straight line has been given.

Acquired bacterial resistance is one of the causes of mortality and morbidity from infectious diseases. Mathematical modeling allows us to predict the spread of resistance and to some extent to control its dynamics. The purpose of this review was to examine existing mathematical models in order to understand the pros and cons of currently used approaches and to build our own model. During the analysis, seven articles on mathematical approaches to studying resistance that satisfied the inclusion/exclusion criteria were selected. All models were classified according to the approach used to study resistance in the presence of an antibiotic and were analyzed in terms of our research. Some models require modifications due to the specifics of the research. The plan for further work on model building is as follows: modify some models, according to our research, check all obtained models against our data, and select the optimal model or models with the best quality of prediction. After that we would be able to build a model for the development of resistance using the obtained results.

Abstract-The paper is devoted to classification of MEDLINE abstracts into categories that correspond to types of medical interventions -types of patient treatments. This set of categories was extracted from Clinicaltrials.gov web site. Few classification algorithms were tested including Multinomial Naive Bayes, Multinomial Logistic Regression, and Linear SVM implementations from sklearn machine learning library. Document marking was based on the consideration of abstracts containing links to the Clinicaltrials.gov Web site. As the result of an automatical marking 3534 abstracts were marked for training and testing the set of algorithms metioned above. Best result of multinomial classification was achieved by Linear SVM with macro evaluation precision 70.06%, recall 55.62% and F-measure 62.01%, and micro evaluation precision 64.91%, recall 79.13% and F-measure 71.32%.