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Theoretical and experimental investigations of water vapor interaction with porous materials are carried out both at the macro level and at the micro level. At the macro level, the influence of the arrangement structure of individual pores on the processes of water vapor interaction with porous material as a continuous medium is studied. At the micro level, it is very interesting to investigate the dependence of the characteristics of the water vapor interaction with porous media on the geometry and dimensions of the individual pore.

In this paper, a study was carried out by means of mathematical modelling of the processes of water vapor interaction with suffering pore of the cylindrical type. The calculations were performed using a model of a hybrid type combining a molecular-dynamic and a macro-diffusion approach for describing water vapor interaction with an individual pore. The processes of evolution to the state of thermodynamic equilibrium of macroscopic characteristics of the system such as temperature, density, and pressure, depending on external conditions with respect to pore, were explored. The dependence of the evolution parameters on the distribution of the diffusion coefficient in the pore, obtained as a result of molecular dynamics modelling, is examined. The relevance of these studies is due to the fact that all methods and programs used for the modelling of the moisture and heat conductivity are based on the use of transport equations in a porous material as a continuous medium with known values of the transport coefficients, which are usually obtained experimentally.

The proteins of the Major Histocompatibility Complex (MHC) play a key role in the functioning of the adaptive immune system, and the identification of peptides that bind to them is an important step in the development of vaccines and understanding the mechanisms of autoimmune diseases. Today, there are a number of methods for predicting the binding of a particular MHC allele to a peptide. One of the best such methods is NetMHCpan-4.0, which is based on an ensemble of artificial neural networks. This paper presents a methodology for qualitatively improving the underlying neural network underlying NetMHCpan-4.0. The proposed method uses the ensemble construction technique and adds as input an estimate of the Potts model taken from static mechanics, which is a generalization of the Ising model. In the general case, the model reflects the interaction of spins in the crystal lattice. Within the framework of the proposed method, the model is used to better represent the physical nature of the interaction of proteins included in the complex. To assess the interaction of the MHC + peptide complex, we use a two-dimensional Potts model with 20 states (corresponding to basic amino acids). Solving the inverse problem using data on experimentally confirmed interacting pairs, we obtain the values of the parameters of the Potts model, which we then use to evaluate a new pair of MHC + peptide, and supplement this value with the input data of the neural network. This approach, combined with the ensemble construction technique, allows for improved prediction accuracy, in terms of the positive predictive value (PPV) metric, compared to the baseline model.

A promising method of increasing precipitation in arid climates is the method of creating a vertical high-temperature jet seeded by hygroscopic aerosol. Such an installation makes it possible to create artificial clouds with the possibility of precipitation formation in a cloudless atmosphere, unlike traditional methods of artificial precipitation enhancement, which provide for increasing the efficiency of precipitation formation only in natural clouds by seeding them with nuclei of crystallization and condensation. To increase the power of the jet, calcium chloride, carbamide, salt in the form of a coarse aerosol, as well as NaCl/TiO₂ core/shell novel nanopowder, which is capable of condensing much more water vapor than the listed types of aerosols, are added. Dispersed inclusions in the jet are also centers of crystallization and condensation in the created cloud to increase the possibility of precipitation. To simulate convective flows in the atmosphere, a mathematical model of FlowVision large-scale atmospheric flows is used, the solution of the equations of motion, energy and mass transfer is carried out in relative variables. The statement of the problem is divided into two parts: the initial jet model and the FlowVision large-scale atmospheric model. The lower region, where the initial high-speed jet flows, is calculated using a compressible formulation with the solution of the energy equation with respect to the total enthalpy. This division of the problem into two separate subdomains is necessary in order to correctly carry out the numerical calculation of the initial turbulent jet at high velocity (M > 0.3). The main mathematical dependencies of the model are given. Numerical experiments were carried out using the presented model, experimental data from field tests of the installation for creating artificial clouds were taken for the initial data. A good agreement with the experiment is obtained: in 55% of the calculations carried out, the value of the vertical velocity at a height of 400 m (more than 2 m/s) and the height of the jet rise (more than 600 m) is within an deviation of 30% of the experimental characteristics, and in 30% of the calculations it is completely consistent with the experiment. The results of numerical simulation allow evaluating the possibility of using the high-speed jet method to stimulate artificial updrafts and to create precipitation. The calculations were carried out using FlowVision CFD software on SUSU Tornado supercomputer.

The influence of the process of initiating a rapid local heat release near surface streamlined by supersonic gas (air) flow on the separation region that occurs during a fast turn of the flow was investigated. This surface consists of two planes that form obtuse angle when crossing, so that when flowing around the formed surface, the supersonic gas flow turns by a positive angle, which forms an oblique shock wave that interacts with the boundary layer and causes flow separation. Rapid local heating of the gas above the streamlined surface simulates long spark discharge of submicrosecond duration that crosses the flow. The gas heated in the discharge zone interacts with the separation region. The flow can be considered two-dimensional, so the numerical simulation is carried out in a two-dimensional formulation. Numerical simulation was carried out for laminar regime of flow using the sonicFoam solver of the OpenFOAM software package.

The paper describes a method for constructing a two-dimensional computational grid using hexagonal cells. A study of grid convergence has been carried out. A technique is given for setting the initial profiles of the flow parameters at the entrance to the computational domain, which makes it possible to reduce the computation time by reducing the number of computational cells. A method for non-stationary simulation of the process of rapid local heating of a gas is described, which consists in superimposing additional fields of increased pressure and temperature values calculated from the amount of energy deposited in oncoming supersonic gas flow on the corresponding fields of values obtained in the stationary case. The parameters of the energy input into the flow corresponding to the parameters of the electric discharge process, as well as the parameters of the oncoming flow, are close to the experimental values.

During analyzing numerical simulation data it was found that the initiation of rapid local heating leads to the appearance of a gas-dynamic perturbation (a quasi-cylindrical shock wave and an unsteady swirling flow), which, when interacting with the separation region, leads to a displacement of the separation point downstream. The paper considers the question of the influence of the energy spent on local heating of the gas, and of the position on the streamlined surface of the place of heating relative to the separation point, on the value of its maximum displacement.

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.

During the last several decades, cardiovascular disease has surpassed all others as the leading cause of mortality in both high-income and low-income countries. The mortality rate from heart disorders may be lowered with early identification and close clinical monitoring. However, it is not feasible to adequately monitor patients every day, and 24-hour consultation with a doctor is not a feasible option, since it requires more sagacity, time, and knowledge than is currently available.

In this study, we examine the Explainable Artificial Intelligence (XAI) technique, namely, the SHAP interpretability approach, in order to educate the medical professionals about the Explainable AI (XAI) methods that can be helpful in healthcare. The XAI methods enhance the trust and understandability of both practitioners and Health Researchers in AI Models. In this work, we propose a composite Deep Learning model: Bi-LSTM+CNN model to effectively predict heart disease from patient data. After balancing the dataset, the Bi-LSTM+CNN model was used. In contrast to other studies, our proposed hybrid deep learning model produced excellent experimental results, including 99.05% accuracy, 99% precision, 99% recall, and 99% F1-score.

The article contains results of modeling of bi-modal microstructure evolution of VT6 alloy during isothermal forging. The model of recrystallization based on dislocation approach to nucleation and growth of nuclear was made to calculate recrystallization processes of the secondary alpha phase. The globularization process of lamellar alpha phase was calculated with assumption of diffusion-control migration of beta-phase boundary due to grain boundary diffusion of vanadium atom. The theoretical results were compared with experimental one.

In the paper the model for a one-dimensional non-equilibrium riverbed process is proposed. The model takes into account the suspended and bed-load sediment transport. The bed-load transport is determined by using the original formula. This formula was derived from the thin bottom layer motion equation. The formula doesn’t contain new phenomenological parameters and takes into account the influence of bed slope, granulometric and physical mechanical parameters on the bed-load transport. A number of the model test problems are solved for the verification of the proposed mathematical model. The comparison of the calculation results with the established experimental data and the results of other authors is made. It was shown, that the obtained results have a good agreement with the experimental data in spite of the relative simplicity of the proposed mathematical model.

The article deals with the process of creating an effective algorithm for determining the amount of emitted quanta from an X-ray tube in computer tomography (CT) studies. An analysis of domestic and foreign literature showed that most of the work in the field of radiometry and radiography takes the tabulated values of X-ray absorption coefficients into account, while individual dose factors are not taken into account at all since many studies are lacking the Dose Report. Instead, an average value is used to simplify the calculation of statistics. In this regard, it was decided to develop a method to detect the amount of ionizing quanta by analyzing the noise of CT data. As the basis of the algorithm, we used Poisson and Gauss distribution mathematical model of owns’ design of logarithmic value. The resulting mathematical model was tested on the CT data of a calibration phantom consisting of three plastic cylinders filled with water, the X-ray absorption coefficient of which is known from the table values. The data were obtained from several CT devices from different manufacturers (Siemens, Toshiba, GE, Phillips). The developed algorithm made it possible to calculate the number of emitted X-ray quanta per unit time. These data, taking into account the noise level and the radiuses of the cylinders, were converted to X-ray absorption values, after which a comparison was made with tabulated values. As a result of this operation, the algorithm used with CT data of various configurations, experimental data were obtained, consistent with the theoretical part and the mathematical model. The results showed good accuracy of the algorithm and mathematical apparatus, which shows reliability of the obtained data. This mathematical model is already used in the noise reduction program of the CT of own design, where it participates as a method of creating a dynamic threshold of noise reduction. At the moment, the algorithm is being processed to work with real data from computer tomography of patients.

In this paper, we proposed a two-dimensional chemo-mechanical model of the growth of invasive carcinoma in epithelial tissue. Each cell is modeled by an elastic polygon, changing its shape and size under the influence of pressure forces acting from the tissue. The average size and shape of the cells have been calibrated on the basis of experimental data. The model allows to describe the dynamic deformations in epithelial tissue as a collective evolution of cells interacting through the exchange of mechanical and chemical signals. The general direction of tumor growth is controlled by a pre-established linear gradient of nutrient concentration. Growth and deformation of the tissue occurs due to the mechanisms of cell division and intercalation. We assume that carcinoma has a heterogeneous structure made up of cells of different phenotypes that perform various functions in the tumor. The main parameter that determines the phenotype of a cell is the degree of its adhesion to the adjacent cells. Three main phenotypes of cancer cells are distinguished: the epithelial (E) phenotype is represented by internal tumor cells, the mesenchymal (M) phenotype is represented by single cells and the intermediate phenotype is represented by the frontal tumor cells. We assume also that the phenotype of each cell under certain conditions can change dynamically due to epithelial-mesenchymal (EM) and inverse (ME) transitions. As for normal cells, we define the main E-phenotype, which is represented by ordinary cells with strong adhesion to each other. In addition, the normal cells that are adjacent to the tumor undergo a forced EM-transition and form an M-phenotype of healthy cells. Numerical simulations have shown that, depending on the values of the control parameters as well as a combination of possible phenotypes of healthy and cancer cells, the evolution of the tumor can result in a variety of cancer structures reflecting the self-organization of tumor cells of different phenotypes. We compare the structures obtained numerically with the morphological structures revealed in clinical studies of breast carcinoma: trabecular, solid, tubular, alveolar and discrete tumor structures with ameboid migration. The possible scenario of morphogenesis for each structure is discussed. We describe also the metastatic process during which a single cancer cell of ameboid phenotype moves due to intercalation in healthy epithelial tissue, then divides and undergoes a ME transition with the appearance of a secondary tumor.