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Reliable plant disease diagnosis requires not only high classification accuracy, but also stable generalization and interpretable decision-making. Although attention mechanisms are useful to these deep learning models, the performance also depends on where and how they are integrated into the network architecture. This study presents a design-optimized YOLO11m-based classification framework that systematically investigates the impact of Convolutional Block Attention Module (CBAM) injection at different architectural levels for plant disease diagnosis. We perform a comparative modelcontrolled analysis of three model architectures: (i) the baseline YOLO11m-Cls architecture lacking attention, (ii) only adding the backbone block along with CBAM and (iii) a hybrid architecture that includes reduced backbone attention combined with CBAM added at classification head. All models are trained and tested under the same experimental settings using a largescale dataset with around 90 000 images of 38 types of plant diseases. Experimental results clearly show that the uniform injection of CBAM into the backbone reduces stability but causes generalization to worsen with a higher validation loss and significantly lower Top-1 accuracy (≈ 90.5%), while the hybrid attention design balances stability and discrimination, with Top-1 accuracy up to 99.71%, Top-5 accuracy up to 99.99% and near-baseline validation behavior respectively Grad-CAMbased interpretability analysis also demonstrates that the hybrid model generates enhanced and biologically interpretable activation maps, which are more disease-specific with less distraction from background. Notably, the aim of our work is not for achieving superior performance over all classifiers but instead only to provide design-level evidence on how placing attention modulates rigidity and interpretability in YOLO-based classification models. The results provide practical architectural considerations for building dependable and interpretable AI systems in agriculture.

Recently we have developed a new method for studying DNA based on ultrasound - induced cleavage of DNA sugar-phosphate backbone. Relative cleavage rates of the phosphodiester bonds in all 16 dinucleotides have been determined. The increased amount of data sampling (of more than 20 000 nucleotides) made it also possible to obtain cleavage rates in all 256 possible tetranucleotides. These values quantitatively characterize sequence effects on conformational dynamics of DNA sugar phosphate backbone. Same type of DNA heterogeneity have been discovered and studied using its chemical cleavage induced by various chemical agents and DNAse I. The presence of essential heterogeneity in structural properties of DNA might be a key for physical mapping of the genomes, i.e. determining the structural profiles being responsible for DNA recognition by gene expression regulation machinery.

This paper focuses on the problem of modeling and analyzing dynamic regimes, both regular and chaotic, in systems of coupled populations in the presence of random disturbances. The discrete Ricker model is used as the initial deterministic population model. The paper examines the dynamics of two populations coupled by migration. Migration is proportional to the difference between the densities of two populations with a coupling coefficient responsible for the strength of the migration flow. Isolated population subsystems, modeled by the Ricker map, exhibit various dynamic modes, including equilibrium, periodic, and chaotic ones. In this study, the coupling coefficient is treated as a bifurcation parameter and the parameters of natural population growth rate remain fixed. Under these conditions, one subsystem is in the equilibrium mode, while the other exhibits chaotic behavior. The coupling of two populations through migration creates new dynamic regimes, which were not observed in the isolated model. This article aims to analyze the dynamics of corporate systems with variations in the flow intensity between population subsystems. The article presents a bifurcation analysis of the attractors in a deterministic model of two coupled populations, identifies zones of monostability and bistability, and gives examples of regular and chaotic attractors. The main focus of the work is in comparing the stability of dynamic regimes against random disturbances in the migration intensity. Noise-induced transitions from a periodic attractor to a chaotic attractor are identified and described using direct numerical simulation methods. The Lyapunov exponents are used to analyze stochastic phenomena. It has been shown that in this model, there is a region of change in the bifurcation parameter in which, even with an increase in the intensity of random perturbations, there is no transition from order to chaos. For the analytical study of noise-induced transitions, the stochastic sensitivity function technique and the confidence domain method are used. The paper demonstrates how this mathematical tool can be employed to predict the critical noise intensity that causes a periodic regime to transform into a chaotic one.

X-ray diffraction experiment allows determining of magnitudes of complex coefficients in the decomposition of the studied electron density distribution into Fourier series. The determination of the lost in the experiment phase values poses the central problem of the method, namely the phase problem. Some methods for solving of the phase problem result in a set of feasible solutions. Cluster analysis method may be used to investigate the composition of this set and to extract one or several typical solutions. An essential feature of the approach is the estimation of the closeness of two solutions by the map correlation between two aligned Fourier syntheses calculated with the use of phase sets under comparison. An interactive computer program ClanGR was designed to perform this analysis.

The model of market pricing in duopoly describing the prices dynamics as a two-dimensional map is presented. It is shown that the fixed point of the map coincides with the local Nash-equilibrium price in duopoly game. There have been numerically identified a bifurcation of the fixed point, shown the scheme of transition from periodic to chaotic mode through a doubling period. To ensure the sustainability of local Nashequilibrium price the controlling chaos mechanism has been proposed. This mechanism allows to harmonize the economic interests of the firms and to form the balanced pricing policy.

Our purpose is to study the spatio-temporal population wave behavior observed in the predator-prey system. It is assumed that predators move both directionally and randomly, and prey spread only diffusely. The model does not take into account demographic processes in the predator population; it’s total number is constant and is a parameter. The variables of the model are the prey and predator densities and the predator speed, which are connected by a system of three reaction – diffusion – advection equations. The system is considered on an annular range, that is the periodic conditions are set at the boundaries of the interval. We have studied the bifurcations of wave modes arising in the system when two parameters are changed — the total number of predators and their taxis acceleration coefficient.

The main research method is a numerical analysis. The spatial approximation of the problem in partial derivatives is performed by the finite difference method. Integration of the obtained system of ordinary differential equations in time is carried out by the Runge –Kutta method. The construction of the Poincare map, calculation of Lyapunov exponents, and Fourier analysis are used for a qualitative analysis of dynamic regimes.

It is shown that, population waves can arise as a result of existence of directional movement of predators. The population dynamics in the system changes qualitatively as the total predator number increases. А stationary homogeneous regime is stable at low value of parameter, then it is replaced by self-oscillations in the form of traveling waves. The waveform becomes more complicated as the bifurcation parameter increases; its complexity occurs due to an increase in the number of temporal vibrational modes. A large taxis acceleration coefficient leads to the possibility of a transition from multi-frequency to chaotic and hyperchaotic population waves. A stationary regime without preys becomes stable with a large number of predators.

State-of-the-art localization and mapping approaches for augmented (AR) and mixed (MR) reality devices are based on the extraction of local features from the camera. Along with this, modern AR/MR devices allow you to build a three-dimensional mesh of the surrounding space. However, the existing methods do not solve the problem of global device co-localization due to the use of different methods for extracting computer vision features. Using a space map from a 3D mesh, we can solve the problem of collaborative global localization of AR/MR devices. This approach is independent of the type of feature descriptors and localisation and mapping algorithms used onboard the AR/MR device. The mesh can be reduced to a point cloud, which consists of only the vertices of the mesh. We propose an approach for collaborative localization of AR/MR devices using point clouds that are independent of algorithms onboard the device. We have analyzed various point cloud registration algorithms and discussed their limitations for the problem of global co-localization of AR/MR devices indoors.

Predicting novel drug properties is fundamental to polypharmacology, repositioning, and the study of biologically active substances during the preclinical phase. The use of machine learning, including deep learning methods, for the identification of drug – target interactions has gained increasing popularity in recent years.

The objective of this study was to develop a method for recognizing psychotropic effects and drug mechanisms of action (drug – target interactions) based on an analysis of the bioelectrical activity of the brain using artificial intelligence technologies.

Intracranial electroencephalographic (EEG) signals from rats were recorded (4 channels at a sampling frequency of 500 Hz) after the administration of psychotropic drugs (gabapentin, diazepam, carbamazepine, pregabalin, eslicarbazepine, phenazepam, arecoline, pentylenetetrazole, picrotoxin, pilocarpine, chloral hydrate). The signals were divided into 2-second epochs, then converted into $2000\times 4$ images and input into an autoencoder. The output of the bottleneck layer was subjected to classification and clustering using t-SNE, and then the distances between resulting clusters were calculated. As an alternative, an approach based on feature extraction with dimensionality reduction using principal component analysis and kernel support vector machine (kSVM) classification was used. Models were validated using 5-fold cross-validation.

The classification accuracy obtained for 11 drugs during cross-validation was $0.580 \pm 0.021$, which is significantly higher than the accuracy of the random classifier $(0.091 \pm 0.045, p < 0.0001)$ and the kSVM $(0.441 \pm 0.035, p < 0.05)$. t-SNE maps were generated from the bottleneck parameters of intracranial EEG signals. The relative proximity of the signal clusters in the parametric space was assessed.

The present study introduces an original method for biopotential-mediated prediction of effects and mechanism of action (drug – target interaction). This method employs convolutional neural networks in conjunction with a modified selective parameter reduction algorithm. Post-treatment EEGs were compressed into a unified parameter space. Using a neural network classifier and clustering, we were able to recognize the patterns of neuronal response to the administration of various psychotropic drugs.

The paper presents a computational model of the thermal state of the breast with an interstitial tumor. The model is based on the modified Pennes biothermal equation and describes a five-layered biological area including skin, subcutaneous fat, glandular and muscular tissues, as well as a neoplasm zone. Convective heat exchange with the environment is taken into account at the outer boundary, and body temperature is maintained at the internal boundary. In addition, the fabric surface is exposed to exponentially attenuating effects of spatial heating, such a heating scheme is actually based on the Bouguer – Lambert – Baer law. Tissue thermal conductivity and blood perfusion are modeled by linear functions of temperature, reflecting physiological thermoregulation. The boundary-value problem for the partial differential equation has been solved numerically using an explicit-implicit finite difference scheme; the system of algebraic equations getting after an approximation of the mentioned boundary-value problem is solved by the Thomas procedure. Numerical experiments have shown that even a small tumor increases the local temperature of tissues by half a degree due to increased metabolism and delayed blood perfusion. This anomaly is clearly manifested in tumors larger than ten millimeters. It was found that the depth of occurrence critically affects the thermal response: when the tumor is located closer to the surface, the maximum temperature shifts to the skin, whereas at a deeper position, a thermal peak forms inside the glandular tissue. The effectiveness of hyperthermic exposure was assessed by the integral criterion of thermal necrosis based on the Arrhenius law. At a radiation intensity that creates a surface thermal load of about five kilowatts per square meter and an attenuation factor of one hundred, tumor destruction begins after two to three minutes of exposure, while the surrounding healthy tissues remain within safe temperatures. Reducing the attenuation coefficient leads to the opposite effect: heat spreads deeper, and the glandular tissue is damaged first, which limits the therapeutic window. Additionally, maps of the distribution of temperature, time to necrosis, and the depth of thermal damage were constructed depending on the irradiation power, diameter, and position of the tumor.

The problem is constructing the differences that arise on ${\mathrm{\LaTeX}}$ documents editing. Each document is represented as a parse tree whose nodes are called tokens. The smallest possible text representation of the document that does not change the syntax tree is constructed. All of the text is splitted into fragments whose boundaries correspond to tokens. A map of the initial text fragment sequence to the similar sequence of the edited document corresponding to the minimum distance is built with Hirschberg algorithm A map of text characters corresponding to the text fragment sequences map is cunstructed. Tokens, that chars are all deleted, or all inserted, or all not changed, are selected in the parse trees. The map for the trees formed with other tokens is built using Zhang–Shasha algorithm.