A hydraulic drive invariant to reversal loads with a counterbalance valve is developed. The dependencies of static, dynamic, and energy characteristics on the counterbalance valve design parameters are found. The design parameters of the counterbalance valve for a hydraulic drive invariant to reversal loads are optimized.

This study presents a decentralized shared actor–critic framework for cooperative multi-robot coverage in continuous two-dimensional simulation. The method combines permutation-invariant local observations, continuous differential-drive control, and reward shaping based on stepwise Hungarian assignment distances, collision penalties, and time efficiency. Homogeneous teams of four, five, and six agents are evaluated in an obstacle-free environment using five independent training seeds. In the final training window, the full reward configuration achieved full-team success rates of 98.2 ± 2.9% for four agents, 85.1 ± 18.0% for five agents, and 96.3 ± 2.0% for six agents, with mean landmark coverage above 96% in all cases. The lower mean in the five-agent setting was associated with higher seed-level variability dominated by one low-success seed. Reward ablations without assignment shaping or collision penalties remained viable, and seed-level tests did not show a statistically significant final-window advantage of the full reward configuration. The full configuration reached the 80% rolling-success threshold earlier in median terms, with the clearest seed-level support in the four-agent setting. Within-environment comparison showed higher full-team success than MADDPG and MAPPO under the matched training horizon and final-window protocol. Deterministic arena-size transfer from $15\times 15$ to $30\times 30$ showed decreasing full-team success as arena size increased, while partial landmark coverage remained higher than strict full-team completion. The results support the method for small homogeneous teams in the tested obstacle-free simulation, while larger teams, external obstacles, aerial-robot dynamics, formal safety guarantees, and hardware deployment remain future work.

Forecasting stock prices has always been a complex and challenging task due to its high volatility, noise in data, and abrupt price fluctuations caused by different factors. Traditional approaches, such as quantitative, fundamental, and technical analysis, often struggle to accurately predict stock price movements due to their difficulty in processing non-linear data. The evolution of machine learning, particularly neural network models, opened new opportunities for improving prediction accuracy by handling non-linear and multidimensional data with high effectiveness. This study focuses on evaluating and comparing three neural network architectures, in which the last is a combination of the first two - Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model, for forecasting stock prices. Using ten years of historical stock data, taken for Apple Inc. (AAPL) stocks as an example, ranging from 2015 to 2025, the models were trained and tested on key performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R². Results show that while both CNN and LSTM models show good performance individually, the hybrid CNN-LSTM model can significantly outperform them, achieving the highest accuracy with the lowest error rates. By combining the strengths of CNN for feature extraction and LSTM for temporal sequence processing, the hybrid model effectively captures complex patterns in stock price movements. By iterating over the types of layers to use, we came to the conclusion, that by combining both models into one hybrid model, some layers, like one CNN layer, and one LSTM layer should be removed. This research demonstrates the potential of hybrid neural network models in financial time-series forecasting and comprehensively analyzes their advantages over non-hybrid models.

В данной статье рассмотрены алгоритмы распознавания станций подзарядки и оптимизации траекторий полета беспилотных летательных аппаратов (БПЛА) с применением методов Reinforcement Learning (RL). Представлены традиционные подходы компьютерного зрения и глубокого обучения для идентификации зарядных станций, а также алгоритмы планирования маршрутов, включая A*, Dijkstra и Rapidly Exploring Random Tree. Проведен сравнительный анализ методов и показано, что RL обладает высокой адаптивностью в реальных условиях, обеспечивая улучшение автономности и энергоэффективности БПЛА. Исследование демонстрирует, как интеграция RL способствует повышению точности стыковки дронов с зарядными станциями и оптимизации их работы в динамической среде.