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Coffee berry disease (CBD), resulting from the Colletotrichum kahawae fungal pathogen, poses a severe risk to coffee crops worldwide. Focused on coffee berries, it triggers substantial economic losses in regions relying heavily on coffee cultivation. The devastating impact extends beyond agricultural losses, affecting livelihoods and trade economies. Experimental insights into coffee berry disease provide crucial information on its pathogenesis, progression, and potential mitigation strategies for control, offering valuable knowledge to safeguard the global coffee industry. In this paper, we investigated the mathematical model of coffee berry disease, with a focus on the dynamics of the coffee plant and Colletotrichum kahawae pathogen populations, categorized as susceptible, exposed, infected, pathogenic, and recovered (SEIPR) individuals. To address the system of nonlinear differential equations and obtain semi-analytical solution for the coffee berry disease model, a novel analytical approach combining the Shehu transformation, Akbari – Ganji, and Pade approximation method (SAGPM) was utilized. A comparison of analytical results with numerical simulations demonstrates that the novel SAGPM is excellent efficiency and accuracy. Furthermore, the sensitivity analysis of the coffee berry disease model examines the effects of all parameters on the basic reproduction number $R_0$. Moreover, in order to examine the behavior of the model individuals, we varied some parameters in CBD. Through this analysis, we obtained valuable insights into the responses of the coffee berry disease model under various conditions and scenarios. This research offers valuable insights into the utilization of SAGPM and sensitivity analysis for analyzing epidemiological models, providing significant utility for researchers in the field.

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.

When modeling turbulent flows in practical applications, it is often necessary to carry out a series of calculations of bodies of similar topology. For example, bodies that differ in the shape of the fairing. The use of convolutional neural networks allows to reduce the number of calculations in a series, restoring some of them based on calculations already performed. The paper proposes a method that allows to apply a convolutional neural network regardless of the method of constructing a computational mesh. To do this, the flow field is reinterpolated to a uniform mesh along with the body itself. The geometry of the body is set using the signed distance function and masking. The restoration of the flow field based on part of the calculations for similar geometries is carried out using a neural network of the UNet type with a spatial attention mechanism. The resolution of the nearwall region, which is a critical condition for turbulent modeling, is based on the equations obtained in the nearwall domain decomposition method.

A demonstration of the method is given for the case of a flow around a rounded plate by a turbulent air flow with different rounding at fixed parameters of the incoming flow with the Reynolds number $Re = 10^5$ and the Mach number $M = 0.15$. Since flows with such parameters of the incoming flow can be considered incompressible, only the velocity components are studied directly. The flow fields, velocity and friction profiles obtained by the surrogate model and numerically are compared. The analysis is carried out both on the plate and on the rounding. The simulation results confirm the prospects of the proposed approach. In particular, it was shown that even if the model is used at the maximum permissible limits of its applicability, friction can be obtained with an accuracy of up to 90%. The work also analyzes the constructed architecture of the neural network. The obtained surrogate model is compared with alternative models based on a variational autoencoder or the principal component analysis using radial basis functions. Based on this comparison, the advantages of the proposed method are demonstrated.

The problem of ligand binding to certain receptor proteins is of central importance in cellular signaling, but it is still unresolved at a molecular level. In order to enhance our understanding of the molecular mechanisms we used a biophysical approach to study a serotonin-gated ion channel. The molecular model of 5-HT3 receptor extracellular domain was created using computer-based homology modeling. The docking method was used for building complexes of the 5-HT3 receptor and ligands. Some different activities were investigated by the method of molecular dynamics.

We consider a model of populations that are closely related and share a common areal. System of nonlinear parabolic equations is formulated that incorporates nonlinear diffusion and migration flows induced by nonuniform densities of population and carrying capacity. We employ the method of lines and study the impact of migration on scenarios of local competition and coexistence of species. Conditions on system parameters are determined when a nontrivial family of steady states is formed.

In this paper particularities of the swirling turbulent flow of monodisperse suspension in the hydrocyclone with injector are investigated on the base of the numerical simulation. The 2D axisymmetric approximation of Reynolds Stresses Model and model of mixture is used to describe the swirling turbulent flow field of suspension and particles parameters in the hydrocyclone. Special attention is paid to the clarification of mechanisms of injection influence on the reorganization of hydrodynamic field and finally on classification mechanisms. It is shown that tangential injection method stronger effects separation curve compared to the radial one.

A model, describing the influence of external factors on temporal evolution of phytoplankton distribution in a horizontally-homogenous water layer, is presented. This model is based upon the reactiondiffusion equation and takes into account the main factors of influence: mineral nutrients, insolation and temperature. The mineral nutrients and insolation act oppositely on spatial phytoplankton distribution. The results of numerical modeling are presented and the prospect of applying this model to reconstruction of phytoplankton distribution from sea-surface satellite data is discussed. The model was used to estimate the chlorophyll content of the Peter the Great Bay (Sea of Japan).

Comparative analysis of two models of porous medium (Dacry and Brinkman) on an example of mathematical simulation of transient natural convection in a porous vertical cylindrical cavity with heat-conducting shell of finite thickness in conditions of convective cooling from an environment has been carried out. The boundary-value problem of mathematical physics formulated in dimensionless variables such as stream function, vorticity and temperature has been solved by implicit finite difference method. The presented verification results validate used numerical approach and also confirm that the solution is not dependent on the mesh size. Features of the conjugate heat transfer problems with considered models of porous medium have been determined.

We consider a time-delayed quadratic discrete model of population dynamics under the influence of random perturbations. Analysis of stochastic attractors of the model is performed using the methods of direct numerical simulation and the stochastic sensitivity function technique. A deformation of the probability distribution of random states around the stable equilibria and cycles is studied parametrically. The phenomenon of noise-induced transitions in the zone of discrete cycles is demonstrated.

The study is based on a three-dimensional hydrodynamic global climate coupled model, including ocean model with real depths and continents configuration, sea ice evolution model and energy and moisture balance atmosphere model. Aerosol concentration from the year 2010 to 2100 is calculated as a controlling parameter to stabilize mean year surface air temperature. It is shown that by this way it is impossible to achieve the space and seasonal uniform approximation to the existing climate, although it is possible significantly reduce the greenhouse warming effect. Climate will be colder at 0.1–0.2 degrees in the low and mid-latitudes and at high latitudes it will be warmer at 0.2–1.2 degrees. The Pareto frontier is investigated and visualized for two parameters — atmospheric temperature mean square deviation for the winter and summer seasons. The Pareto optimal amount of sulfur emissions would be between 23.5 and 26.5 TgS/year.