## 1 Introduction  
The first accepted research paper by [PERSON] and [PERSON] considers a rendezvous game, where players move at different speeds and markers can be left by one of the players on the infinite line. For its solution, an LP-based formulation is suggested. The authors showed how the search space can be reduced to a space of significantly smaller dimensions by making the enumeration of all elements realistic. The next paper by [PERSON] et al. presents a method for tackling fault detection and diagnosis in a cellular network. The authors use two datasets made up of performance support system data and drive test data. They also present a framework for identifying the need for handovers. By applying the dynamic neural network method, great accuracy was achieved. Then, [PERSON] et al. present a study on the generalization of a known family of multi-point scalar iterative processes for approximating solutions of non-linear systems. A convergence analysis under different smooth conditions is given. They also investigate stability, analyze the fixed and critical points of the resulting rational operator, and present graphical analyses of dynamical planes, parameter lines, and bifurcation planes. Finally, numerical tests are produced for various non-linear systems in order to check the obtained theoretical results and to compare the proposed schemes with existing ones. In the next paper, [PERSON] and [PERSON] deal with the setting of hyperparameters in optimization algorithms and propose a novel learning scheme. Their approach differs from existing ones and exploits the learning and generalization capabilities of artificial neural networks, with the goal of adapting a general setting using automatic configurators. The suggested approach is tested on two algorithms: one algorithm that is very sensitive to parameter settings applied to instances of the generalized assignment problem, and a robust tabu search algorithm applied to instances of the quadratic assignment problem. In both cases, the approach turned out to be effective. The paper by [PERSON] et al. deals with the optimization of the formation and use of a machine and tractor fleet within a crop farming enterprise. The authors discuss the concepts and indicators that characterize the process of agricultural operations related to said machine fleet. In particular, after presenting the problem and context in detail, an optimization model for executing a complex of mechanized works in agricultural production is presented. Then the authors present a general scheme consisting of five stages. It includes a heuristic algorithm for performing a set of agricultural works, the results of which are compared with those obtained by the GAMS solver. In particular, the authors present results for real data in an agricultural enterprise alongside interpretations of the obtained results. Then, in contribution 6, [PERSON] et al. present a neural network package which has been optimized for the use on a high-performance computing cluster for a future compressed baryonic matter (CBM) experiment at a research facility in Germany. The package has been developed in C++. To identify events containing quark-gluon plasma, fully connected and convolutional neural networks have been created. The tests show the superiority of convolutional neural networks over fully connected networks, and achieved high accuracy with the considered dataset. In the next paper, [PERSON] et al. introduce a system which makes the collection, processing, real-time streaming, and storage of eye tracking data possible. For the development of this system, the Java programming language, WebSocket protocol, and Representational State Transfer were used. The results were obtained under two sets of test conditions, namely local and online scenarios. The results show that this system can significantly support the research community by providing real-time data transfer and storage. Then, [PERSON] et al.