This paper presents an adaptive control framework that enables a unmanned robot to follow its planned trajectory including cases when the reliability of its sensor suite changes unpredictably. The main contribution is the adaptive mode of operation: the system dynamically adjusts the threshold value of each sensor stream, recalculates their weights in real time, and recombines control system. While high precision measurements are available, the controller runs in a nominal linear quadratic mode. When observability drops – because a sensor drifts, saturates, or fails – the algorithm transitions smoothly to a prediction only mode that relies on the process model, and, if necessary, to a robust H-infinity mode that preserves stability under worst case disturbances. The underlying estimation layer is based on an innovation driven weighting rule embedded in an Extended Kalman Filter, making the approach independent of any particular sensor type. Hardware simulation and outdoor testing confirm that adaptive mode significantly improves tracking accuracy, shortens recovery time after faults, and eliminates the need for manual retuning when the sensor configuration changes.

Interval timing, the ability to perceive and estimate durations in the seconds-to-minutes range, represents one of the most fundamental cognitive processes underlying adaptive behavior across species. This capacity enables organisms to extract temporal regularities from their environment, optimize foraging strategies, coordinate communication, and make predictions about future events. The paper describes a model of an active agent with an internal structure. The structure is represented by an ensemble capable of generating rhythmic activity within specific intervals. The ensemble is a network of three nodes: a half-center oscillator consisting of two nodes exciting each other in antiphase, and a trigger node with memory. The half-center oscillator excites the trigger, which, when activated, transmits activity to the agent. The frequency of trigger activation depends on its threshold value. A population timing model composed of such agents is proposed, demonstrating properties characteristic of biological systems: adherence to Weber’s Law, shift in duration estimates toward mean values, forgetting in the absence of a stimulus, and a return to homeostatic parameters. Biologically inspired timing research not only advances our understanding of time perception, evaluation, and prediction but also drives innovations in adaptive AI, robotics, and human-machine interfaces.

The chapter investigates the problem of cross-lingual plagiarism in academic works of European universities. Although the possibly massive problem of incorrect text reuse, most text reuse detection systems generally focus only on the monolingual plagiarism text reuse: when both the analysed document and source of text reuse are written in one language. In this chapter, we analyse a more difficult setting: when the languages of the analysed document and reused language are different. For this problem solution, we present a system of cross-lingual text reuse detection. The system composes the methods of statistical machine translation and deep learning methods based on the contextualized word embeddings, such as BERT and its multilingual version, LaBSE. To analyse the efficiency of the proposed method, we conduct experiments both on the synthetic dataset generated using machine translation systems and on the real dataset of academic graduation theses. We experimented on the collection of 10202 documents and found 103 documents with a significant amount of cross-lingual text reuse. Although these results are preliminary and should be verified further, they confirm the massiveness of this problem in academic science.

We find two Lax representations for the reduced magnetohydrodynamics equations ({\sc rmhd}) and construct a local variational Poisson structure (a Hamiltonian operator) for them. Its inverse defines a nonlocal symplectic structure for the same equations. We describe the action of both operators on the second-order cosymmetries and on the infinitesimal contact symmetries of {\sc rmhd}, respectively. The reduction of {\sc rmhd} by the symmetry of shifts along the $z$-axis coincides with the equations of two-dimensional ideal magnetohydrodynamics ({\sc imhd}). Applied to the Lax representations and the variational Poisson structure of {\sc rmhd}, the reduction provides analogous constructions for {\sc imhd}.

The encoding of temporal information is a basic function of biological systems that underlies perception, learning, decision-making, and behavioral synchronization. Despite an extensive array of empirical data and a variety of theoretical models, there is still no unified concept of temporal encoding. The report provides an overview of publications dedicated to research on the memorization, representation, and reproduction of time intervals by living organisms, as well as the modeling of time encoding. The modern understanding of neural correlates of time encoding is considered. The evolution of approaches and models is traced: from classical models of the internal chronometer to more complex network, population, and Bayesian concepts. Key trends and paradigm shifts in modeling memorization and prediction of time are outlined. The key properties of time encoding observed in most vertebrate species are indicated. Two fundamental models are described in detail: the internal clock model and the Scalar Expectancy Theory. Their significance for control theory, artificial intelligence, and robotics is justified.