This article discusses an approach to developing digital twins for the food industry based on monitoring and numerical modeling of thermodynamic processes in a baking chamber (electric oven). The professional electric oven ASTAR, designed for the thermal processing of bakery products, was used as the experimental object. As part of the monitoring system, an infrared pyrometer (VICTOR 304F), a thermal imaging camera (UNI-T UTi120S), and an analog thermometer (MGprof) installed inside the oven chamber were employed. This setup allowed for the acquisition of reliable temperature data within the working chamber. Temperature data were collected periodically throughout the baking process, and based on these measurements, a temperature field map was generated. A mathematical model of heat transfer, implemented in two-dimensional (2D) format using MATLAB PDE Toolbox and incorporating Dirichlet and Neumann boundary conditions, was validated through comparison with the experimental results. The obtained results not only enable accurate modeling of temperature gradients and heat fluxes inside the baking chamber, but also lay the foundation for the creation of a digital twin capable of predicting system behavior in real time. The proposed approach can be applied to improve energy efficiency, automate quality control, and optimize technological processes in the food industry. This research contributes to addressing the challenges encountered in modeling and designing thermodynamic processes during the development of digital twins.

In large-scale cloud environments, virtualization plays a key role not only in application deployment and workload distribution but also in the effective management of platform services and resources. The use of containerization technology, alongside traditional approaches such as virtual machines, is becoming increasingly popular in modern cloud infrastructures. Containers provide a simpler and faster method of virtualization compared to virtual machines, accelerating the deployment and management of applications in isolated environments and thereby enabling more efficient resource utilization. For developers, containers offer a convenient solution for testing application components and building microservice-based architectures. As a result, containerization is becoming the preferred solution for modern cloud environments and current business needs. Therefore, this study is dedicated to examining modern and widely recognized container technologies. The article analyzes contemporary containerization and orchestration technologies using parameters such as container performance, scalability, and resource management. Particular attention is given to orchestrators such as Docker Swarm, Kubernetes, and Apache Mesos, as well as a range of container-based solutions, with an evaluation of the advantages and disadvantages of each technology. This work may be useful for IT specialists in selecting the most appropriate tools for application development and the implementation of their cloud strategies.

Ischemic stroke is one of the leading causes of mortality and disability. Accurate segmentation of damaged regions in brain CT images is critical for timely diagnosis and clinical decision-making. In this study, an ensemble approach is proposed, combining SE-UNETR and Swin UNETR transformer models via weighted voting. The Dice coefficient was used for evaluation, measuring the overlap between predicted lesion regions and reference annotations. Unlike single-model approaches, ensemble neural network methods provide higher reliability and segmentation accuracy by integrating predictions from multiple architectures. Three-dimensional CT scans of 98 patients with acute ischemic stroke, provided by the International Tomography Center of the Siberian Branch of the Russian Academy of Sciences, were used. The results demonstrated that the proposed ensemble outperforms individual models. The average Dice coefficient was 0.7983, indicating the high effectiveness of the method in segmenting ischemic lesions. Analysis showed that the ensemble approach more accurately delineates lesion boundaries in brain CT images and reduces segmentation errors. The proposed method can be applied not only to stroke but also to other pathologies requiring precise medical image analysis in automated diagnostic systems.

The analysis of tonality in scientific texts, including citations, is actively advancing, enabling the identification of emotional coloring in references and their impact on scientific discourse. This study focuses on developing and evaluating a hybrid approach that integrates linguistic rules (analysis of parts of speech, syntactic dependencies, and negations) with machine learning algorithms (SVM, RF, NB, J48) to classify citation tonality. Experiments were conducted on the ACL Anthology (8700 sentences) and Clinical Trials (6500 additional sentences) corpora using stratified splitting (70/15/15 for train/val/test) and 5-fold cross-validation. The proposed method achieved 90% macro-F1 and 95% F1-score on the Athar dataset, and 85% macro-F1 on Clinical Trials, showing a 10–15% improvement over baseline models (BERT, LSTM). Ablation studies confirmed the contribution of linguistic rules (F1 increase of 5–7% when excluded). Statistical significance tests (McNemar, p<0.05) validated the robustness of the results. The approach proves effective for automated citation analysis and scientific impact assessment.

One of the modern directions in the field of information security is post-quantum cryptography. Its purpose is to develop new quantum-resistant cryptographic algorithms. An important section of post-quantum cryptography is electronic digital signature algorithms. There are a number of different approaches to designing post-quantum signatures. One of the main approaches to designing post-quantum cryptographic digital signature algorithms is hash-based post-quantum digital signature schemes. Hash-based post-quantum digital signature schemes are one of the main types of post-quantum cryptographic digital signature algorithms. They are quite efficient and provably secure. Their reliable security has been established against both classical and quantum attacks. There are many types of digital signatures for solving various information security problems, such as group signatures, ring signatures, blind signatures, verifiably encrypted signatures, etc. This paper proposes a new cryptographic protocol of verifiably encrypted signature with possibility of verification after a specified time based on the post-quantum cryptographic algorithm of hash-based digital signature, TANBA-SPHINCS+. The protocol is an efficient combination of the postquantum digital signature algorithm TANBA-SPHINCS+, the cryptographic protocol providing data encryption for a specified time, ECTLC, and the verifiably encrypted signature scheme.

The paper considers a parameterized formulation of the subset sum problem for an -element set of positive integers, where the sum of the selected elements equals a given certificate . A method for constructing subsets of fixed cardinality based on index certificates and two-dimensional arrays is proposed, enabling efficient subset selection without exhaustive enumeration. The core idea is to shift from the value space to the index space and to exploit structured array diagonals, which reduces the practical computational complexity for fixed . Algorithms for constructing subsets of even cardinality are presented, and their applicability to information retrieval and big data processing tasks is demonstrated, including unstructured information search, where processing speed and efficient memory usage are critical. It is emphasized that the obtained results correspond to a restricted and parameterized formulation of the problem and do not contradict the known NP-completeness of the general subset sum problem.

Modern trends in the digitalization of education contribute to the active introduction of technology into the process of teaching writing, especially at the preschool stage. The article presents an intelligent system for analyzing the formation of graphomotor skills in preschool children based on mathematical modeling of errors when writing letters using touch devices. The purpose of the research is to develop and test a mathematical model for estimating graphomotor errors, which makes it possible to identify and analyze typical deviations from the standard when performing tasks in the Dexterous Fingers digital application. The model is based on comparing the user’s trajectory with the reference one, represented as piecewise linear functions. Smoothing using the Catmull-Rum spline is used to improve the accuracy of the analysis. A system of metrics is proposed: shape deviation, angular deviation, offset of the start/end points, and similarity metric (Frechet distance). These parameters form an integral assessment of the quality of the task. The app automatically generates progress reports and graphs for educators and parents, as well as provides recommendations for corrective actions. The developed interface visualizes errors, forms recommendations and records progress. This approach significantly expands the possibilities of diagnosing and correcting writing skills, complementing traditional methods of teacher supervision. Thus, the smart electronic application “Dexterous Fingers” is an effective tool for digital pedagogical diagnostics, contributing to the early detection of writing disorders and improving preschool children’s readiness for school education.

Accurate prediction of IT project costs is crucial for successful project planning, budgeting, and resource allocation. However, typical cost estimation methods, such as Function Point Analysis, or expert-based evaluations, frequently fail to produce trustworthy conclusions, especially in developing countries like Kazakhstan where previous project data is few or incomplete. This study looks into how ensemble machine learning algorithms, notably Random Forest and Gradient Boosting, can be used to predict IT project costs when there is insufficient data available. To solve data shortage, this study applies synthetic data creation techniques, which result in extended datasets that model various project scenarios while retaining statistical features observed in real-world cases. The presented models use essential project variables, such as team size, project complexity, development process, and project size, as inputs for cost prediction. Experimental results show that ensemble approaches outperform standard estimating techniques in terms of predictive accuracy. Random Forest achieved the lowest mean absolute error (MAE = 0.09) and highest coefficient of determination (R² = 0.603). Furthermore, feature importance analysis shows that project size and development time are the most important elements in cost estimation. The findings demonstrate ensemble learning’s usefulness in dealing with complicated, nonlinear connections among project variables, as well as providing a feasible approach for improving cost estimation techniques in the absence of high-quality historical data. This work adds to the development of intelligent decision support systems and offers practical insights for IT project managers and policymakers in emerging economies who want to improve IT project budgeting and planning.

In recent years, Kazakhstan has faced serious challenges, both in terms of water shortages and the operation of existing hydraulic structures. Droughts have systematically reduced accumulated freshwater reserves, while the 2024 flood has led to the inundation of several settlements. To understand the true state of existing hydraulic structures and the water reserves in these reservoirs, as well as to forecast potential risks, the government has initiated a series of studies. In light of the increased urgency of understanding the condition of hydraulic structures, the authors address the issue of assessing the accuracy of computer modeling of the region’s reservoirs (using the Karatomar and Verkhne-Tobol reservoirs as examples). In this study, the bathymetry of the Karatomar reservoir was obtained using an Apache 3 drone. Kriging methods were used to study the relationship between modeling accuracy and line frequency. Reservoir models were built in QGIS and Surfe. Sentinel-2 satellite imagery and data from the Kazvodkhoz Agency (Kazvodkhoz) were used for shoreline analysis. The study resulted in an algorithm for determining the density (pitch) of hydrodrone lines for modern bottom geomorphology. The research showed that the accuracy of surveys of even lowland hydraulic structures in Kazakhstan significantly depends on the field survey parameters, and the hydraulic structures themselves have undergone significant changes over the course of their operation.

This paper employs cutting-edge control strategies, including PID regulation, to manage the dynamic and time-sensitive processes inherent in cement manufacturing. The Honeywell C300 controller is utilized to implement a robust and scalable system capable of adapting to the demands of high temperatures, material flow variations, and operational disturbances. Mathematical modeling and simulation tools, such as MATLAB, are used to analyze the system’s stability, as well as to obtain control parameters, allowing for predictive and adaptive management of crucial variables such as temperature and flow rate. This effort is important for more than just improving operational efficiency; it also contributes to sustainability by optimizing energy use and reducing waste. By integrating with worldwide initiatives to lessen the environmental effect of industrial processes, the system illustrates how automation may transform the cement manufacturing process. This article explores the control system’s technological underpinnings, design techniques, and practical implementations, providing insights into its transformational potential.

The article presents a comprehensive study of the application of machine learning methods for automated analysis of radiographic images of the respiratory system for the early detection of pathological changes. A method for classifying pulmonary diseases based on an ensemble of deep convolutional neural networks, including the DenseNet121, MobileNetV2, EfficientNetB0, SENet, and ShuffleNetV2 architectures, is proposed and implemented. The study included a comparative analysis of the effectiveness of various image preprocessing methods, including the use of raw black-and-white X-ray images without additional processing, the use of the CLAHE (Contrast Limited Adaptive Histogram Equalization) method in combination with color filtering, and the use of the DynamicCNN neural network denoiser for noise suppression. Experimental results showed that the ensemble approach using the soft voting strategy provides a statistically significant improvement in classification accuracy compared to individual models. The obtained results confirm the high efficiency of the proposed approach and demonstrate the potential of using ensemble deep learning models in medical diagnostics and clinical decision support tasks.

The research introduces a deep neural network system which achieves multi-class emotion classification through its development process. The system identifies seven emotional states through its classification system which includes angry, disgust, fear, happy, neutral, sad and surprise. The researchers divided their dataset into training and testing parts after preprocessing and they used precision and recall and F1-score and confusion matrix and ROC-AUC curves to evaluate their results. The model achieves its highest accuracy when detecting happy emotions at 89% followed by surprise at 68% and disgust at 49% according to the confusion matrix. The model achieves good to excellent classification results for most emotions yet it struggles with “fear and neutral emotions because their features overlap or their class distributions are unbalanced. The researchers computed Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) values for each class in the study. The model produced its best AUC results for happy and surprise emotions at 0.92 and 0.90 respectively followed by disgust at 0.84. The lowest AUC score of 0.71 appeared in the fear category because this emotion showed weak discriminative properties. The model achieved a macro-averaged AUC score of 0.82 when evaluating all classes together. The proposed neural network shows strong performance in emotion recognition tasks through its ability to detect intense emotions such as happiness and surprise.

The expansion of telecommunication services has been met with a rise in the cases of the fraudulent phone calls posing a big threat to individuals and organizations. Conventional techniques of detecting are usually based on offline analysis of full conversations, which restricts their promptness of intervention. In this paper, the author proposes a real-time, turn-taking, fraud detecting system, which is based on pre-trained contextual embeddings in combination with a bi-directional Long Short-Term Memory network in order to model semantic content and temporal dynamics of multi-turn conversations. To detect fraudulent calls, the system progressively changes the probability of a call being a fraud after every conversational turn to allow it to detect a fraud. When tested with a synthetic multi-turn dialogue dataset, it is shown that the proposed BiLSTM using BERT embeddings has a test accuracy of 93.75% and an F 1 score of 93.55, which is higher than the current machine learning and convolutional baselines. The system can note most of the scams during the initial few turns of a call, which offers fast risk evaluation. These findings suggest the usefulness of context-based, progressing modeling to detect fraud in real time and its possibility of practical application.

This study addresses the problem of inefficient task scheduling and limited fault tolerance in ad-hoc grid computing environments, where traditional systems rely on centralized control and stable infrastructure. To overcome this, a decentralized agent behavior was developed using the Java Agent DEvelopment Framework, enabling autonomous task redistribution among heterogeneous Worker agents. The proposed Scheduler–Worker architecture allows dynamic coordination and failure recovery without centralized orchestration. Experiments on five devices show that increasing the number of agents from one to three reduces total execution time by 1.98–3.25, while the best performance is achieved with four agents, providing a 2.99 speedup for 100 tasks. However, using six agents on fewer devices reduces efficiency to 2.34 due to resource contention and communication overhead. The study is limited by a single network topology and a small-scale testbed. Nevertheless, the results demonstrate the practical potential of agent-based decentralized scheduling for resilient distributed machine learning systems.

The evolution of 5G and the anticipated introduction of 6G technologies significantly increases network complexity through service-based architecture, network slicing, virtualization and distributed cloud-native functions. These advancements improve scalability and flexibility but simultaneously expand the attack surface and introduce novel vulnerabilities. Traditional penetration testing methodologies are not suitable for such dynamic and virtualized environments because they rely on static procedures and manual testing that cannot match the speed and structural variability of modern mobile networks. In parallel, machine learning–based intrusion detection systems (IDS) demonstrate strong capabilities in detecting anomalous and zero-day behaviors but operate independently from penetration testing processes. This paper presents an intelligent hybrid method for automated penetration testing in 5G and beyond networks, integrating a machine-learning intrusion detection system based on incremental learning, autoencoders and generative adversarial networks (GANs) – with an attack optimization module driven by the Differential Evolution (DE) algorithm. Instead of a genetic algorithm, DE is employed due to its fast convergence, robustness to local minima, and suitability for optimizing high-dimensional representations of attack strategies. An experimental evaluation on a real OpenAirInterface-based 5G Standalone testbed demonstrates that the DEdriven approach improves vulnerability identification efficiency, produces optimal multi-stage attack strategies, and enables realistic automated penetration scenarios. These results indicate that DE-based optimization provides a scalable, adaptive and efficient foundation for continuous security assessment of next-generation mobile networks.

Global optimization of multi-extremal, multi-variable functions is an important problem for the development of various areas of science. Its relevance is that the need to search for global extremum of functions constantly arises both in theoretical research and in practice. In this work, a new method for determining the global minimum of a multi-extremal, multi-variable function is proposed. In a previously published work by one of the authors of this article, a special function called the "auxiliary function" was constructed by transforming the objective function, and its important properties (non-negativity, uniform discontinuity, differentiability, monotonicity, etc.) were studied. In the presented article, necessary and sufficient conditions for the global minimum of the objective function are rigorously formulated and proven. As a result, the problem of finding the global minimum of a multi-extremal and multi-variable function was reduced to the problem of determining the "greatest zero" of a convex function of one variable: it was proved that the global minimum of the objective function is equal to the exact upper bound of the zeros of the auxiliary function. And the problem of rational application of known numerical methods to determine the "greatest zero" of the auxiliary function with high accuracy was considered.

The Cauchy problem at point 0, where the characteristic equation built according to the homogeneous part of the linear integro-differential equation under consideration has both positive and negative roots, and the order of the derivative of the integral uo to 2, has never been examined before in the theory of equations with small parameters. It is well known that the problem’s solution may veer towards infinity when the characteristic equation’s roots are opposite. The Cauchy problem for a differential equation with a small parameter in front of two higherorder derivatives is still uncertain when the roots of the characteristic equation are opposite. This can be solved analytically by adding the integral part to the right-hand side of the differential equation and treating it as an integrodifferential equation. In this paper, an unperturbed problem is constructed for a given perturbed problem with a small parameter. In the unperturbed problem the external differential operator is one order lower than the internal differential operator. This is a non-standard case and requires special consideration. In this regard, an analytical formula for the solution of the unperturbed problem is obtained, and further analysis is carried out. Moreover, the interrelation between the perturbed and unperturbed problems was established and illustrated by an example. The theorem on the limiting transition was also formulated.

This paper considers a boundary value problem for a fractional-order integro-differential equation 0<α<1 with an involutivity transformation. To investigate the solvability of the boundary value problem, we use the parameterization method proposed by Professor D. Dzhumabaev. This is done by introducing a parameter μ = x(0) and changing variables. x (t ) = u (t ) +μ This change of variables formally divides the problem into two parts: a Cauchy problem for a fractional-order integro-differential equation with an involutive transformation 0 < 𝛼𝛼 < 1 and a linear equation with respect to the introduced parameter. By determining the solution to the Cauchy problem for a fractional-order integro-differential equation with an involutive transformation 0 < 𝛼𝛼 < 1 and substituting it into the boundary condition, we obtain a linear equation with respect to the introduced parameter. Assuming that the coefficient of this equation is nonzero, we find a unique solution to the boundary value problem. A relationship is established between the solvability of the problem and the coefficient of the resulting equation.

In recent years, grand Lebesgue spaces, grand Lorentz spaces, and their generalizations have been extensively studied in functional analysis. This is because it has become evident that most known functional spaces are insufficient for modeling applied problems such as electrorheological fluids, thermorheological fluids, image processing, differential equations with non-standard growth, and other fields. Therefore, new precise scales of functional spaces have been introduced, namely variable exponent spaces and grand spaces. In this article, using the definition of anisotropic grand Lorentz spaces and their previously proven properties, we derive a previously unproven Hölder's inequality in this space. To prove these inequalities, we utilize the properties of decreasing rearrangements of functions. The study employs methodologies developed for multidimensional cases, including the analysis of relationships between ordered and rearranged versions of functions. The duality of these spaces is established by means of Hölder’s inequality. The results obtained are not only of theoretical importance but also find applications in practical problems, such as solving differential equations and studying integral operators. The findings presented in this article contribute to deepening the theory of functional spaces and expanding their areas of application.

An initial–boundary value problem is considered for a differential equation with n loads, depending on two variables and involving fourth-order partial derivatives. By introducing a new unknown function, the original problem is reduced to a family of Cauchy problems for a loaded differential equation with first-order partial derivatives. For the unknown function with loads z(t_i,x), a system of functional equations with respect to the variable x is constructed. An algorithm for finding a solution to this system of functional equations is proposed. A theorem on the existence and uniqueness of a solution to the family of Cauchy problems for the loaded differential equation with first-order partial derivatives is proved. Conditions for the existence and uniqueness of a solution to the initial–boundary value problem for the loaded differential equation involving fourth-order partial derivatives and depending on two variables are established. The result is illustrated by an example.

This paper presents a numerical method for reconstructing the spatial distribution of sound speed in inhomogeneous media based on the inverse analysis of acoustic wave propagation. The mathematical model relies on the second-order wave equation with variable coefficients. The inverse problem is formulated as an optimization task to minimize the residual functional between simulated and observed pressure data at the domain boundaries. To efficiently calculate the gradient of the functional, an adjoint (auxiliary) problem method is employed, derived via variational calculus. The numerical implementation is performed using an explicit finite-difference scheme. Computational experiments on a one-dimensional model of a heterogeneous medium (soil-metal-soil) demonstrate that the proposed algorithm allows for reliable reconstruction of the velocity profile, particularly in zones of sharp contrast. The study analyzes the sensitivity of the solution and the convergence rate, showing that 500 iterations provide an optimal balance between accuracy and computational cost.

В данной статье рассматриваются оценки коэффициентов Фурье–Уолша для функций двух переменных, обладающих ограниченной вариацией. Исследование направлено на получение верхних оценок модулей коэффициентов ряда Фурье–Уолша, что позволяет анализировать сходимость и аппроксимационные свойства соответствующих рядов. Основное внимание уделено функциям, заданным на единичном квадрате, и имеющим ограниченную вариацию по каждой переменной и в совокупности. Приводятся оценки, зависящие от индексов коэффициентов и характеристик вариации функции. В статье получены новые верхние оценки модулей коэффициентов ряда Фурье–Уолша для функций двух переменных с ограниченной вариацией. В отличие от классических результатов в работе получены новые верхние оценки коэффициентов Фурье–Уолша для функций двух переменных с учетом как вариации по каждой переменной, так и их совместной вариации. Такой подход позволяет более точно описывать поведение коэффициентов и частичных сумм рядов, что важно для исследования абсолютной сходимости и аппроксимационных свойств в многомерном случае. Актуальность работы обусловлена современными направлениями развития теории ортогональных рядов и их прикладными аспектами. Ряды Фурье–Уолша широко применяются в цифровой обработке сигналов, теории сжатия и восстановления данных, а также при анализе дискретных и двоичных структур, что в последние годы приобретает особую значимость в связи с развитием цифровых технологий и вычислительных методов.

This paper presents a numerical study of the relativistic restricted three-body problem within the framework of General Relativity. Using the Lagrangian and Hamiltonian formalisms, the equations of motion with relativistic corrections up to the order of 1/c2 were derived and solved numerically in Wolfram Mathematica. The developed model allows one to analyze orbital stability under small relativistic perturbations. Numerical simulations were performed using the Runge–Kutta integration method for three systems: “Earth–Sun–Moon,” “Earth–Sun– Mercury,” and an equal-mass configuration. The results confirm the stability of circular orbits and reproduce the observed relativistic precession of Mercury’s perihelion. For the equal-mass system, the calculations reveal a transition from quasi-periodic to chaotic motion, depending on the initial conditions. The study demonstrates the reliability and efficiency of the Mathematica environment for modeling nonlinear relativistic dynamics and shows that the proposed approach can be useful for further research in celestial mechanics and gravitational physics.

High-mass X-ray binaries (HMXBs) are commonly divided into persistent sources, which emit X-rays steadily over long periods, and transient sources, which remain mostly in a quiescent state and exhibit episodic X-ray outbursts. The aim of this work is to statistically compare the X-ray luminosity properties of these two classes, focusing on the maximum luminosity and the luminosity variability range . For this purpose, catalog data for Galactic HMXB pulsars were used, including 18 persistent and 64 transient systems. The distributions of and were analyzed using histograms, box plots, cumulative distribution functions (CDFs), and the non-parametric Mann–Whitney test. It is shown that both classes reach similar values of on the order of – erg/s), with no statistically significant difference between their distributions. However, transient systems exhibit a much wider luminosity variability range, reaching increases of 4–5 orders of magnitude (up to ), whereas persistent sources typically show lower variability. This difference is statistically confirmed, with transients demonstrating significantly higher variability p ≈ 0.05). Thus, the main distinction between persistent and transient HMXBs lies not in their peak X-ray luminosity, but in their accretion regime. In persistent systems, the compact object continuously accretes matter from a relatively stable stellar wind of an OB supergiant, resulting in steady X-ray emission. In contrast, transient systems are characterized by intermittent accretion (for example, episodic mass capture from a Be-star circumstellar disc), which leads to their extreme luminosity variability.

Carbon nanowalls (CNWs) are promising carbon nanomaterials for flexible and wearable electronic applications due to their unique vertically oriented architecture and high electrical conductivity. In this work, the influence of mechanical deformation on the electrical properties of flexible CNW films was systematically investigated. CNWs were synthesized by inductively coupled plasma–enhanced chemical vapor deposition and transferred onto polymer substrates for electromechanical testing. Hall effect measurements revealed that increasing bending strain and cyclic mechanical loading result in a gradual increase in sheet resistance accompanied by a decrease in electrical conductivity and charge carrier mobility, while the carrier concentration remains nearly unchanged. Scanning electron microscopy showed the formation of deformation-induced microcracks and partial disruption of conductive pathways after repeated bending, whereas the overall nanowall morphology was largely preserved. Raman spectroscopy confirmed the stability of the sp2 carbon framework, with an increased defect-related signal after deformation. The strong correlation between electrical, morphological, and spectroscopic results demonstrates that defect accumulation governs the electromechanical response of CNWs. These findings highlight the mechanical robustness of CNWs and their suitability for flexible electronic and sensing devices.

In this paper, we study the crystallization of amorphous indium selenide (InSe) films during heat treatment in an inert atmosphere. The films were obtained by thermal evaporation of bulk stoichiometric InSe crystals under conditions of high vacuum. The film structure was analyzed using Raman spectroscopy and X-ray diffraction analysis, the results of which indicate the initial amorphous phase that transforms into a stoichiometric InSe structure with a hexagonal crystal lattice under heat treatment at 350°C. A feature of the studied films is a significant contrast in the specific electrical resistance between the amorphous and crystalline states. The transition to the crystalline phase is accompanied by a sharp decrease in electrical resistance – by several orders of magnitude, which makes it possible to indirectly determine the crystallization temperature of the films by measuring the temperature dependence of the sample’s resistance. In this work, a sharp change in resistance was found in the region of ~ 140°C, corresponding to the crystallization temperature of the studied samples. The obtained results confirm the possibility of forming a polycrystalline InSe film on large areas, which in turn is important when creating prototypes of optoelectronic devices. Furthermore, in this work, the feasibility of local crystallization of InSe thin films was demonstrated using laser patterning technique.

We present high–angular resolution 1.3 mm continuum and molecular line observations toward the highmass star-forming region G350.29+0.12, using CH₃CN and CH₃¹³CN rotational transitions. The continuum emission resolves two main cores: a bright, compact northern core G350.29+0.12 A and a weaker southern core G350.29+0.12 B. Core A exhibits six compact substructures embedded within more extended emission. M K-components of the CH3CN J = 14→13 and CH3 13CN J = 14→13 transitions are detected, with the emission arising primarily from core A. The CH3CN moment 0 maps show that the integrated intensities peak at the main continuum position, indicating that the molecular emission traces warm and dense gas. The moment 1 maps reveal a pronounced velocity gradient of ∼2 km s-1 across core A, oriented from northwest to southeast, while the moment 2 maps show K-dependent variations in velocity dispersion. Position–velocity diagrams further indicate organized rotational motions, exhibiting a compact velocity structure with a central peak and systematic gradients. Rotational temperature analysis yields = 360.6 ± 34.8 K for CH3CN, tracing the hottest and densest gas and Trot = 138 ± 45 K for CH3 13CN, consistent with cooler and more extended material. Together, these results demonstrate that G350.29+0.12 A is a rotating hot molecular core undergoing active high-mass star formation.

The study of the mechanisms of star formation and evolution is based on comprehensive research into interstellar regions, including the analysis and identification of young stellar objects (YSOs). The identification of young stellar objects by their radiation at various wavelengths in the infrared range is a relatively recent development - the first studies in this field appeared only at the end of the 20th century. The development of this field has been made possible by improvements in observation techniques and data processing methods, the acquisition of more reliable characteristics of stellar sources, and the creation of catalogs containing extensive arrays of information about cosmic objects. In this study, the star-forming region W40 of the Aquilla molecular cloud was investigated in the infrared wavelength range to detect previously unidentified young stellar objects at various stages of evolution. Identification was carried out using two approaches: photometric criteria and spectral indices. This made it possible to more reliably identify the evolutionary stages of 37 newly discovered and previously unexplored YSO candidates (5 objects of class I, 2 objects of class II, 4 objects – class “transitional disks” and 26 objects — class III), which indicates an active and ongoing process of star formation in W40.

This paper investigates the effect of nickel foam on the structure and properties of Radio-Frequency Dielectric Barrier Discharge (RF-DBD) in an Ar/CH4 mixture at a low pressure of 0.5 Torr. Experiments were conducted by varying the supplied power, gas flow, and distance between the catalyst and the RF electrode. It has been shown that an increase in plasma power and a change in CH4 gas flow in the presence of a catalyst leads to a noticeable shortening of the plasma glow length in a quartz tube. It was found that increasing the distance between the catalyst and the RF electrode reduces the ability to maintain plasma in the region downstream of the catalyst. The analysis of optical emission spectra revealed a decrease in the intensity of carbon-containing radicals, atomic and molecular hydrogen after the catalyst, which indicates its active participation in plasma-catalytic processes. Raman analysis confirmed the formation of amorphous carbon deposits on the nickel foam surface. It has been established that nickel foam not only modifies the structure of the RF-DBD discharge, but also significantly affects the distribution of active particles in the plasma, changing the conditions for plasma-catalytic reactions. The results obtained provide a deeper understanding of the mechanisms of interaction between low-temperature plasma and porous metal catalysts and can be used in the development of effective plasma-catalytic systems for the conversion of hydrocarbon gases.

In this paper, a numerical study of a typical section of an overhead main gas pipeline with reinforced composite linings mounted on supports for the analysis of vibration frequencies is carried out. The study was conducted using the finite element method in the ANSYS Workbench software package. The study considers cases with internal working and critical pressure as a load. The result of the study showed that in the variation of the installation of composite linings under the condition of critical pressure, the first eleven oscillation frequencies showed a lower value, and starting from the twelfth frequency, lower values were shown in the operating pressure condition. At the same time, under operating pressure conditions, the location of the composite lining in the variation between supports 2 and 3, as well as in the variation between supports 3 and 4, showed the same results, that is, the change occurred only in 1,4 and 5 forms. In the variation, when the pad is installed between the supports 1 and 2, the shapes 1,3 and 5 were changed. Under conditions of critical pressure, the location of the composite lining in the variation between supports 2 and 3, as well as in the variation between supports 1 and 5, showed the same results, that is, the change occurred in 1,2 and 5 forms. In the variation, when the pad is installed between the supports 3 and 4, the shapes 1,3 and 5 were changed. When comparing all three cases according to the values of the oscillation frequencies, it was established that the value of the oscillation frequencies when installing composite linings in the middle in most cases exceed, that is, of the twenty frequencies listed, 60-70% of the chatot index is higher relative to the other two cases. Thus, the obtained research results can be used to select the location of a carbon fiber lining for banding gas pipelines in a region with seismic activity.

In the development of oil and gas fields with elevated and high formation temperatures, the effectiveness of hydraulic fracturing (HF) operations is largely determined by the correct selection of the breaker system, which ensures controlled degradation of the polysaccharide gel after pumping and proppant placement. In conventional HF design practice, breaker type and concentration are typically selected based on a static formation temperature, while variations in the near-wellbore thermal regime during pumping are only partially considered. This study presents the results of an analysis of formation temperature dynamics recorded in real time using an autonomous downhole pressure–temperature gauge during an HF operation, including the injection test, mini-frac, and main fracturing stages. It was established that injection of fracturing fluid at a surface temperature of approximately 20 °C leads to a rapid reduction of near-wellbore temperature by several tens of degrees relative to the initial formation temperature. Operational pauses result in partial temperature recovery; however, the original thermal state is not restored before subsequent stages. The results demonstrate that the effective temperature governing HF fluid performance during a significant portion of the operation differs substantially from the static formation temperature. This discrepancy affects gel degradation kinetics and the efficiency of persulfate-type oxidative breakers. Comparison of field temperature data with laboratory rheological test results obtained using a Chandler 5550 viscometer confirms the necessity of accounting for dynamic temperature conditions when selecting breaker systems. Incorporating actual thermal history into breaker design improves selection reliability and enhances fracture cleanup efficiency in hightemperature reservoirs.

Polymer flooding is widely implemented as a mobility-control method for enhanced oil recovery (EOR); however, incremental recovery beyond mobility improvement is frequently reported and remains incompletely explained. This review examines the role of viscoelasticity in improving micropore sweep efficiency during polymer flooding. A clear distinction is made between micropore access (flow penetration into low-connectivity microdomains) and micropore mobilization (release of trapped oil in dead-end pores and corners). The analysis synthesizes experimental observations, pore-scale simulations, and rheological considerations to evaluate whether elastic stresses contribute to additional oil displacement mechanisms beyond shear viscosity effects. Particular attention is given to extensional flow behavior in converging–diverging pore geometries, the influence of crude oil viscosity, and the role of salinity, temperature, and mechanical degradation in suppressing viscoelastic responses. The review demonstrates that viscoelastic contributions to oil recovery are condition-dependent and most pronounced within specific viscosity and operational windows. Variability in reported results is largely attributed to inconsistent rheological characterization and insufficient consideration of degradation effects. Standardized brine-conditioned rheological protocols, including extensional metrics where feasible, are recommended to improve reproducibility and predictive capability. The findings highlight the need for multi-scale validation linking pore-scale mechanisms to core-scale displacement and field performance.

The Precaspian Basin, including its southeastern part, represents a unique oil and gas province with giant fields such as Tengiz and Kashagan, whose reserves are estimated in billions of tons. Despite extensive research in the region, questions regarding the genetic affiliation of oils and their paleoformation conditions require additional geochemical analyses of oil samples. To conduct a comprehensive geochemical characterization and genetic correlation of oils from the Emba region, with a focus on molecular marker analysis to reconstruct oil formation conditions seven oil samples were taken from various stratigraphic horizons. The study employed advanced analytical techniques gas chromatography (Agilent 7890B) for detailed analysis of n-alkanes and isoprenoids (pristane and phytane) and gas chromatography-mass spectrometry in selected ion monitoring mode m/z 191, 217 for terpane and sterane identification, respectively. Particular emphasis was placed on the computation of essential geochemical indices isoprenoid, hopane, and sterane isomerization ratios along with the generation of diagnostic plots. The results indicate a marine origin of the studied oils with dominant carbonate source rocks (C₂₇ steranes > 30%, C₂₉/C₃₀ hopanes > 1). Pr/Ph ratios (0.79–1.07) and elevated C₃₅ homohopanes suggest formation under reducing conditions. Thermal maturity parameters (Ts/(Ts+Tm) = 0.15–0.64; C₂₉ ββ/(αα+ββ) = 0.55–0.71) confirm that the samples were generated during the oil window. Genetic correlation grouped all samples into a single oil family of marine carbonate origin. The findings are practically significant for predicting analogous accumulations in the region.

This study aimed to analyze the impact of branding on hazardous products such as tobacco, alcohol, and drugs, focusing on consumer preferences, trust, and purchasing behavior. It also examined how these factors varied across different demographics and levels of brand awareness. Additionally, the study explored the ethical implications of branding harmful products and whether national regulations mitigated the adverse effects of such marketing. Experts believed that branding influenced consumer behavior, attracting the attention of researchers, particularly in industries that sold products that negatively impact consumer health. This study focused on the Kazakhstani cigarette and alcohol markets to assess how branding strategies influenced consumer choices within dangerous product categories. By drawing on existing literature on consumer behavior, brand loyalty, and the ethicality of marketing harmful goods, the researcher sought to determine whether branding shaped consumer perception and consumption. A combination of primary and secondary data sources was used, and a survey was conducted to evaluate consumer attitudes toward branding in the Kazakh market. The findings indicated that branding significantly influenced consumer behavior. However, regarding harmful products, certain limitations emerged. With increasing ethical concerns and growing awareness of health risks, branding appeared to be less effective than before. The study was limited by its focus on the Kazakhstani market, which may not fully represent global consumer behavior regarding harmful products. Additionally, the reliance on self-reported survey data introduced the possibility of response bias. This study contributes to the discourse on the ethicality of branding harmful products by providing insights into consumer behavior in an emerging market. It highlights the evolving effectiveness of branding amid rising ethical concerns and regulatory measures, offering valuable implications for policymakers, businesses, and researchers. The study also emphasizes the need for stricter regulations governing the branding of harmful goods.

This research investigates organizational competitiveness in emerging markets driven by global economic integration. It aims to evaluate the relationship between three critical drivers: digital transformation, Global Value Chain (GVC) participation, and strategic agility. A quantitative cross-sectional survey was conducted with 435 senior executives from the logistics, manufacturing, agribusiness, and energy sectors. Using SPSS and R, the study applies regression and mediation models grounded in the Resource-Based View and Dynamic Capabilities Theory to analyze firm-level dynamics. The results indicate that digital maturity and innovation capability significantly enhance competitiveness. Strategic agility acts as a key mediator, translating international integration into improved performance. Firms with deep GVC involvement and digitally integrated supply chains demonstrated superior export results and growth in return on assets (ROA). The study underscores that managers in emerging economies must prioritize digital readiness and agility. These capabilities are essential prerequisites for capitalizing on global integration; failure to invest in them risks creating a competitive disadvantage. Unlike prior macroeconomic studies, this research provides firm-level empirical evidence on how managerial strategies interact with the depth of economic integration. It bridges theoretical insights with the practical realities faced by firms in the global economy.

This study examines leadership styles and project dynamics within Kazakhstan’s petrochemical industry, specifically in the context of the “Nurly Zhol” policy and China’s “Belt and Road Initiative.” Using a sequential mixed-methods approach and a “Macro-Meso-Micro” framework, the research analyzes how stakeholders overcame systemic financial, technical, and pandemic-related disruptions encountered throughout the project’s fourteenyear lifecycle. The research adopts data triangulation, including documentary analysis, 12 stakeholder interviews, and a survey of 68 experts, which reveals a “dual-track” governance mechanism. Findings indicate that informal relational governance and political leadership were essential to compensating for limitations in formal engineering and construction contracts. Furthermore, the study highlights the critical role of cross-cultural communication in navigating these complex partnerships in the environment that exists. Ultimately, project success required aligning transactional discipline with facilitative adaptability, offering a new framework for managing institutional complexity in transitional economies and providing practical insights for future international infrastructure development projects and initiatives.

The contemporary global business landscape is defined by continuous consolidation, where mergers and acquisitions (M&A) serve as a primary growth strategy for organizations aiming to secure technological leadership and market share in emerging economies. However, empirical evidence suggests that a significant proportion of these deals fail to realize their strategic intent due to the systematic neglect of human capital and cultural alignment during the post-integration phase. This article examines the critical aspects of human capital management (HCM) within the framework of a cross-border acquisition of a Kazakhstani telecommunications operator, Company X, by an international conglomerate, Company Y. The research focuses on identifying and diagnosing HR and cultural risks arising within the first 12 months post-deal, a period where deal value is most vulnerable to erosion. Utilizing a mixed-methods approach that triangulates secondary data analysis, in-depth semi-structured interviews (n = 23), and extensive employee sentiment surveys (n = 106), the study investigates the underlying socio-cultural dynamics governing integration success. The results reveal three primary threats: a significant «brain drain» where voluntary turnover rose to 15% (doubling the historical norm), a deep-seated cultural conflict between the «agile» entrepreneurial spirit of the acquired firm and the bureaucratic, centralized structure of the acquirer, and a critical communication vacuum that left 78% of frontline staff uninformed about integration milestones. A fundamental finding is the strong negative correlation (r = –0.72) between the quality of internal communication and key employee attrition risk, providing a quantifiable link between transparency and talent retention. Based on these findings, a comprehensive integration model is proposed, incorporating data-driven tools such as a Retention Risk Matrix and a Communication Heatmap, designed to foster a hybrid corporate culture that preserves local agility while leveraging global scalability.