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The work presents an experimental study of the tree structure that occurs during a medical examination. At each meeting with a medical specialist, the patient receives a certain number of areas for consulting other specialists or for tests. A tree of directions arises, each branch of which the patient should pass. Depending on the branching of the tree, it can be as final — and in this case the examination can be completed — and endless when the patient’s goal cannot be achieved. In the work both experimentally and theoretically studied the critical properties of the transition of the system from the forest of the final trees to the forest endless, depending on the probabilistic characteristics of the tree.

For the description, a model is proposed in which a discrete function of the probability of the number of branches on the node repeats the dynamics of a continuous gaussian distribution. The characteristics of the distribution of the Gauss (mathematical expectation of $x_0$, the average quadratic deviation of $\sigma$) are model parameters. In the selected setting, the task refers to the problems of branching random processes (BRP) in the heterogeneous model of Galton – Watson.

Experimental study is carried out by numerical modeling on the final grilles. A phase diagram was built, the boundaries of areas of various phases are determined. A comparison was made with the phase diagram obtained from theoretical criteria for macrosystems, and an adequate correspondence was established. It is shown that on the final grilles the transition is blurry.

The description of the blurry phase transition was carried out using two approaches. In the first, standard approach, the transition is described using the so-called inclusion function, which makes the meaning of the share of one of the phases in the general set. It was established that such an approach in this system is ineffective, since the found position of the conditional boundary of the blurred transition is determined only by the size of the chosen experimental lattice and does not bear objective meaning.

The second, original approach is proposed, based on the introduction of an parameter of order equal to the reverse average tree height, and the analysis of its behavior. It was established that the dynamics of such an order parameter in the $\sigma = \text{const}$ section with very small differences has the type of distribution of Fermi – Dirac ($\sigma$ performs the same function as the temperature for the distribution of Fermi – Dirac, $x_0$ — energy function). An empirical expression has been selected for the order parameter, an analogue of the chemical potential is introduced and calculated, which makes sense of the characteristic scale of the order parameter — that is, the values of $x_0$, in which the order can be considered a disorder. This criterion is the basis for determining the boundary of the conditional transition in this approach. It was established that this boundary corresponds to the average height of a tree equal to two generations. Based on the found properties, recommendations for medical institutions are proposed to control the provision of limb of the path of patients.

The model discussed and its description using conditionally-infinite trees have applications to many hierarchical systems. These systems include: internet routing networks, bureaucratic networks, trade and logistics networks, citation networks, game strategies, population dynamics problems, and others.

Aberrant alternative splicing of the CD44 gene drives colorectal cancer progression and facilitates the emergence of cancer stem cells. Although biomedical research recognizes this transmembrane glycoprotein as a major catalyst of malignancy, deciphering its multi-isoform regulatory networks remains a complex analytical challenge. To address this knowledge gap, this study presents a machine learning framework designed to decode these biological mechanisms. The author constructed a neural network regressor based on beta regression to model bounded isoform proportions. This computational architecture jointly estimates both the mean and the precision parameters of the underlying probability distribution. Furthermore, the system employs elastic net regularization to perform quantitative feature selection from highdimensional molecular expression data.

The investigation evaluates the proposed framework using gene expression profiles from colorectal cancer patients. The primary objective involves identifying specific ribonucleic acid-binding proteins acting as regulatory splicing factors. The experimental design contrasts two distinct mathematical modeling strategies. The first configuration incorporates an independent ”one-vs-all” approach that treats each transcript variant as an isolated regression target. The second formulation utilizes a structured ”isoform tree” method that directly mirrors hierarchical exon inclusion relationships. Validation experiments on synthetically generated datasets confirmed the mathematical integrity of the network. The model recovered true distribution parameters with precision and exhibited no systematic bias. Comprehensive empirical comparisons subsequently demonstrated that the independent ”one-vs-all” layout consistently outperforms the hierarchical tree configuration in predictive stability and accuracy.

The computational analysis maps the regulatory landscape of the CD44 gene. The framework validates several established splicing factors while uncovering new candidate proteins, including ACO1, NUDT21, and AGO2. Based on these statistical associations, the paper introduces a biological hypothesis. This concept functionally connects intracellular iron metabolism via the ACO1 protein with the shifting balance of CD44 variants. These discoveries provide deeper insights into oncogenic splicing regulation. Ultimately, they highlight molecular targets for future therapeutic interventions aimed at suppressing the cancer stem cell phenotype.

Kinetics of complex formation between components of the photosynthetic electron transport chain — flavodoxin and membrane complex photosystem I has been studied using computer model based on methods of multiparticle simulation and Brownian dynamics. We simulated Brownian motion of several hundreds of flavodoxin molecules, taking into account electrostatic interactions and complex shape of the molecules. Our model could describe experimental nonmonotonic dependence of the association rate constant for flavodoxin and photosystem I. This lets us conclude that electrostatic interactions are sufficient to form such kind of nonmonotonic dependence.

In the article, the questions of application of a optimization method for support of planning processes in warehouse systems by means of simulation are considered. Mechanisms of interrelation of optimization and simulation models are investigated, and also the algorithm of simulation optimization model development of warehouse system for support of planning processes is described in detail.

It was carried out a theoretical analysis of the energy migration rate in the process of non-photochemical quenching of fluorescence pigment-protein complex that performed by means of orange carotenoid-protein in the phycobilisomes of cyanobacteria. It is shown that the observed rate of energy transfer can not be interpreted in the framework of inductive-resonant mechanism of energy migration (Förster’s theory). On the contrary, according to the calculations the implementation of the exciton mechanism is fully consistent with the experimentally observed high quenching rate. An essential feature of the implementation of the exciton mechanism is to comply with a number of structural and functional conditions that require fine-tuning of the molecular system in the interaction of donor and acceptor molecules both each other and with the local molecular environment.

Numerical investigation of mixing non-isothermal streams of sodium coolant in a T-branch is carried out in the FlowVision CFD software. This study is aimed at argumentation of applicability of different approaches to prediction of oscillating behavior of the flow in the mixing zone and simulation of temperature pulsations. The following approaches are considered: URANS (Unsteady Reynolds Averaged Navier Stokers), LES (Large Eddy Simulation) and quasi-DNS (Direct Numerical Simulation). One of the main tasks of the work is detection of the advantages and drawbacks of the aforementioned approaches.

Numerical investigation of temperature pulsations, arising in the liquid and T-branch walls from the mixing of non-isothermal streams of sodium coolant was carried out within a mathematical model assuming that the flow is turbulent, the fluid density does not depend on pressure, and that heat exchange proceeds between the coolant and T-branch walls. Model LMS designed for modeling turbulent heat transfer was used in the calculations within URANS approach. The model allows calculation of the Prandtl number distribution over the computational domain.

Preliminary study was dedicated to estimation of the influence of computational grid on the development of oscillating flow and character of temperature pulsation within the aforementioned approaches. The study resulted in formulation of criteria for grid generation for each approach.

Then, calculations of three flow regimes have been carried out. The regimes differ by the ratios of the sodium mass flow rates and temperatures at the T-branch inlets. Each regime was calculated with use of the URANS, LES and quasi-DNS approaches.

At the final stage of the work analytical comparison of numerical and experimental data was performed. Advantages and drawbacks of each approach to simulation of mixing non-isothermal streams of sodium coolant in the T-branch are revealed and formulated.

It is shown that the URANS approach predicts the mean temperature distribution with a reasonable accuracy. It requires essentially less computational and time resources compared to the LES and DNS approaches. The drawback of this approach is that it does not reproduce pulsations of velocity, pressure and temperature.

The LES and DNS approaches also predict the mean temperature with a reasonable accuracy. They provide oscillating solutions. The obtained amplitudes of the temperature pulsations exceed the experimental ones. The spectral power densities in the check points inside the sodium flow agree well with the experimental data. However, the expenses of the computational and time resources essentially exceed those for the URANS approach in the performed numerical experiments: 350 times for LES and 1500 times for ·DNS.

The biohydrochemical portrait of the White Sea is constructed on the CNPSi-model calculations based on long-term mean annual observations (average monthly hydrometeorological, hydrochemical and hydrobiological parameters of the marine environment) as well as on updated information on the nutrient input to the sea with the runoff of the main river tributaries (Niva, Onega, Northern Dvina, Mezen, Kem, Keret). Parameters of the marine environment are temperature, light, transparency, and biogenic load. Ecological characteristics of the sea “portrait” were calculated for nine marine areas (Kandalaksha, Onega, Dvinsky, Mezensky Bays, Solovetsky Islands, Basin, Gorlot, Voronka, Chupa Bay), these are: the concentration changes of organic and mineral compounds of biogenic elements (C, N, P, Si), the biomass of organisms of the lower trophic level (heterotrophic bacteria, diatomic phytoplankton, herbivorous and predatory zooplankton) and other ones (rates of substance concentration and organism biomass changes, internal and external substance flows, balances of individual substances and nutrients as a whole). Parameters of the marine environment state (water temperature, ratio of mineral fractions N < P) and dominant diatom phytoplankton in the sea (abundance, production, biomass, chlorophyll content a) were calculated and compared with the results of individual surveys (for 1972–1991 and 2007–2012) of the White Sea water areas. The methods for estimating the values of these parameters from observations and calculations differ, however, the calculated values of the phytoplankton state are comparable with the measurements and are similar to the data given in the literature. Therefore, according to the literature data, the annual production of diatoms in the White Sea is estimated at 1.5–3 million tons C (at a vegetation period of 180 days), and according to calculations it is ~2 and 3.5 million tons C for vegetation period of 150 and 180 days respectively.

The paper develops a new mathematical method of the joint signal and noise calculation at the Rice statistical distribution based on combing the maximum likelihood method and the method of moments. The calculation of the sough-for values of signal and noise is implemented by processing the sampled measurements of the analyzed Rician signal’s amplitude. The explicit equations’ system has been obtained for required signal and noise parameters and the results of its numerical solution are provided confirming the efficiency of the proposed technique. It has been shown that solving the two-parameter task by means of the proposed technique does not lead to the increase of the volume of demanded calculative resources if compared with solving the task in one-parameter approximation. An analytical solution of the task has been obtained for the particular case of small value of the signal-to-noise ratio. The paper presents the investigation of the dependence of the sought for parameters estimation accuracy and dispersion on the quantity of measurements in experimental sample. According to the results of numerical experiments, the dispersion values of the estimated sought-for signal and noise parameters calculated by means of the proposed technique change in inverse proportion to the quantity of measurements in a sample. There has been implemented a comparison of the accuracy of the soughtfor Rician parameters’ estimation by means of the proposed technique and by earlier developed version of the method of moments. The problem having been considered in the paper is meaningful for the purposes of Rician data processing, in particular, at the systems of magnetic-resonance visualization, in devices of ultrasonic visualization, at optical signals’ analysis in range-measuring systems, at radar signals’ analysis, as well as at solving many other scientific and applied tasks that are adequately described by the Rice statistical model.

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.

Although the era of exponential performance growth in computer chips has ended, processor core numbers have reached 16 or more even in general-purpose desktop CPUs. As DRAM throughput is unable to keep pace with this computing power growth, CPU designers need to find ways of lowering memory traffic per instruction. The straightforward way to do this is to reduce the miss rate of the last-level cache. Assuming “non-inclusive cache, inclusive directory” (NCID) scheme already implemented, three ways of reducing the cache miss rate further were studied.

The first is to achieve more uniform usage of cache banks and sets by employing hash-based interleaving and indexing. In the experiments in SPEC CPU2017 refrate tests, even the simplest XOR-based hash functions demonstrated a performance increase of 3.2%, 9.1%, and 8.2% for CPU configurations with 16, 32, and 64 cores and last-level cache banks, comparable to the results of more complex matrix-, division- and CRC-based functions.

The second optimisation is aimed at reducing replication at different cache levels by means of automatically switching to the exclusive scheme when it appears optimal. A known scheme of this type, FLEXclusion, was modified for use in NCID caches and showed an average performance gain of 3.8%, 5.4 %, and 7.9% for 16-, 32-, and 64-core configurations.

The third optimisation is to increase the effective cache capacity using compression. The compression rate of the inexpensive and fast BDI*-HL (Base-Delta-Immediate Modified, Half-Line) algorithm, designed for NCID, was measured, and the respective increase in cache capacity yielded roughly 1% of the average performance increase.

All three optimisations can be combined and demonstrated a performance gain of 7.7%, 16% and 19% for CPU configurations with 16, 32, and 64 cores and banks, respectively.