This paper examines the controllability problem of quasilinear integro-differential systems with impulse effects. The influence of weak nonlinear perturbations included in the equations that determine the moments of impulse effects is studied, leading to the necessity of analyzing systems with variable impulse moments. The method of reduction to equations with fixed impulse moments is applied, allowing for the use of classical control approaches. Methods for constructing admissible control that ensures the transition of the system from an initial state to a given final state are developed. The conditions for the existence and uniqueness of solutions, as well as the optimization of control in the mean, aimed at minimizing a given cost functional, are considered. The obtained results can be applied in automatic regulation tasks, modeling of dynamic systems with discrete and continuous influences, as well as in the control of technological and economic processes. This work will be of interest to specialists in differential equations, control theory, and applied mathematics.

The (2+1)-dimensional generalized Benjamin-Ono equation models the propagation of small-amplitude, long-wavelength waves on the surface of shallow water. Constructing explicit solutions of the (2+1)-dimensional generalized Benjamin-Ono equation not only provides theoretical support for experimental investigations but also offers a rigorous basis for addressing applied problems arising in nonlinear wave dynamics. In this work, we investigate wave propagation governed by the (2+1)-dimensional generalized Benjamin-Ono equation in nonlinear media, accounting for dispersive effects. To this end, the sine-cosine function method and the hyperbolic tangent method are employed as analytical tools for deriving explicit solutions. The methods prove effective for a broad class of nonlinear equations encountered in mathematical physics. Using these approaches, periodic-wave solutions and solitary wave solutions are obtained, and to illustrate the obtained results, we plot 3D and 2D plots by setting suitable values of the involved parameters.

Numerous problems arising in various fields of natural sciences and engineering lead to the study of integrodifferential equations. The investigation of such equations, which account for the historical behavior of processes or phenomena, originates from the pioneering works of V. Volterra, where the role of the integral term was emphasized. Various approaches have since been developed to describe hereditary effects and aftereffects in these equations. It should be noted that the existence of multiperiodic and quasiperiodic solutions for systems of Volterra-type integrodifferential equations does not always guarantee their uniqueness. In this paper, a system of partial integro-differential equations with a special differentiation operator is considered. Using the method of periodic characteristics, integral representations of the solution manifold for such systems with aftereffects are constructed. The properties of iterated kernels and resolvents are studied, and corresponding estimates are obtained. Conditions for the existence of a Greentype matrix function for the multiperiodic problem are established, along with integral representations with suitable bounds. Finally, sufficient conditions for the existence and uniqueness of a multiperiodic solution, interpreted as an integral manifold of the integro-differential system with finite hereditary effects, are derived.

The study of local and global invariants of the Rogers semilattice is an important and fundamental problem in numbering theory and computability theory. Global invariants include properties such as an existence of a universal numbering, the number of minimal numberings, the cardinality of the entire semilattice, and a criterion for determining whether a semilattice is a lattice. Local invariants, in turn, describe structures, such as initial segments or intervals within the semilattice. We say that a numbering is universal if any other numbering reduces to . The study of universal numberings is important for understanding the structure of semilattices and their classification. In this paper, an existence of universal numberings is considered for finite families of computably enumerable sets located at finit levels of the Ershov hierarchy. The main result is that for any two-element family of computably enumerable sets , its Rogers semilattice, considered at the third level of the Ershov hierarchy, has universal numberings.

This paper is devoted to the study of a spectral problem for a Sturm–Liouville multiple differentiation operator defined on an interval with nonlocal integrally “perturbed” boundary conditions. The considered boundary conditions are regular but not strongly regular, which leads to essential difficulties in the analysis of the spectral properties and the basis behavior of the corresponding systems of functions. The main objective of the study is to investigate the basis properties of systems of eigen and associated functions in the space of square-summable functions and to analyze their stability and instability under small perturbations of the boundary conditions. As a particular case, the Samarskii–Ionkin problem with integrally perturbed boundary conditions is examined in detail. It is proved that the system of eigen and associated functions of this problem forms a Riesz basis in the L2 space on the interval. The paper also shows that small changes in the integral kernel of the boundary conditions may lead either to the preservation or to the loss of the basis property. Moreover, it is established that the sets of root function systems forming a Riesz basis and the sets of eigen and associated function systems that do not form an ordinary basis are dense in the corresponding functional space L 1. The obtained results contribute to the development of the spectral theory of differential operators with nonlocal boundary conditions.

This article investigates a boundary value problem for an impulsive hyperbolic equation with a piecewise constant argument. Impulsive hyperbolic equations with a piecewise constant argument arise as mathematical models of physical processes in neural networks, continuous dynamical systems, hybrid systems, and other fields. Issues related to the existence of boundary value problems and the construction of their solutions for such equations remain among the most relevant and challenging problems at present. To obtain solvability conditions for the considered problem, the Dzhumabaev parametrization method is employed, and an iterative algorithm for constructing an approximate solution is developed. For each step of the iterative process, integral formulas are derived and expressed in terms of the matrix Q(x), which describes the relationship between the functional parameters. If this matrix is invertible, the existence and uniqueness of the solution are proved both for the parametric and the original problems. The proposed method is not limited to proving theoretical solvability; it also provides a concrete constructive procedure for obtaining the solution. This is of significant importance for subsequent numerical implementations and for the analysis of solution stability. Moreover, the proposed approach can be applied to other types of problems with a piecewise constant argument, including systems with impulsive conditions, neural networks with memory effects, and nonlinear hybrid models.

This paper studies Lie elements and symmetric (Tortken) elements in a free Novikov algebra and examines whether a nonzero multilinear element can belong to both classes simultaneously. We use the Euler operator and the null Lagrangian criterion to test membership in the symmetric subspace for elements represented in the standard multilinear Lie basis. For the Lie component, we employ left-normed commutators with a fixed first variable, which form a convenient basis of the multilinear part. The case is worked out explicitly by expanding the commutators in the Novikov product and applying the Euler operator. For degrees , the corresponding linear systems are obtained and solved computationally in Wolfram Mathematica and Albert. The computations show that the intersection of the multilinear Lie subspace with the subspace of symmetric elements is trivial for all . Thus, up to degree 7 there is no nonzero multilinear element in a free Novikov algebra that is simultaneously Lie and symmetric. These results provide a starting point for studying the problem in higher degrees.

This paper presents a hybrid approach for predicting pollutant dispersion in urban street canyons, taking into account noise barriers. The methodology combines detailed CFD modeling and a surrogate model based on the BiLSTM neural network architecture with an attention mechanism. Configurations with barrier heights of 0.1H, 0.2H, and 0.3H were studied. CFD calculations revealed a nonlinear effect of barrier height on aerodynamics and the formation of pollutant accumulation zones, with the most complex non-stationary behavior observed at a height of 0.2H. The surrogate model successfully predicts concentration evolution for both the barrier-free and 0.1H barrier cases, demonstrating an average absolute percentage error of less than 15%. For a 0.2H barrier, accuracy decreases in zones of intense turbulence due to the highly non-stationary nature of the process. This approach significantly reduces computational costs while maintaining physical accuracy, which is promising for decision support systems in urban ecology. The model provides accelerated forecasting compared to CFD calculations by 7–8 orders of magnitude, so the inference time showed 1–5 ms, while one CFD simulation takes about 54 hours on the CPU.

This paper addresses the solution of the inverse coefficient problem for the wave equation aimed at reconstructing the spatial distribution of the speed of sound in an inhomogeneous medium. The Laplace transform is applied to solve the direct problem, eliminating time dependence and reducing the problem to ordinary differential equations in the frequency domain, which significantly decreases computational costs. The inverse problem is formulated as an optimization task: minimizing the residual functional between calculated and measured acoustic pressure values using the stochastic global optimization method, Differential Evolution. Numerical experiments were conducted on a multilayer medium model (sand, soil, rock, water, air) using synthetic data with added random noise. An adaptive combined reconstruction method is proposed to reduce errors at medium boundaries. The results demonstrate high accuracy: the relative error of the sound speed profile reconstruction was approximately from 2.5 to 4.3%, confirming the approach's effectiveness for acoustic diagnostics and tomography applications.

In this paper, we prove the law of large numbers for a random walk in random scenery. The limiting behavior of such sequences has been intensively studied since the 1980s. Such results, in particular, allow proving the consistency of statistical estimates of unknown parameters in many situations. Unlike previous results, we allow the terms of the random walk, on whose states the random walk in a random scenery is built, to have different distributions and not be centered. We also do not require that the terms of the random walk in a random scenery be identically distributed and independent; it is only required that they have the same mean and be uncorrelated. The research methods are classical methods of probability theory: various probabilistic inequalities (Berry–Esseen, H¨older’s, Lyapunov’s), as well as limit theorems (the central limit theorem, the law of large numbers). It should be noted that the model under consideration has a physical interpretation associated with the motion of a particle in a random environment.

This paper presents a comprehensive multi-stage system designed to improve the accuracy of energy load forecasts and evaluate the effectiveness of both forecast models and demand response (DR) strategies. Using the REFIT dataset, a comparative analysis of a hierarchy of forecast models was conducted, including linear regression, random forest, SVR, k-NN, LSTM, and a hybrid encoder-decoder with an attention mechanism. The results of the study indicated that the developed hybrid encoder-decoder model with an attention mechanism achieved the best accuracy (R² = 0.91, MAPE = 2.39%), demonstrating excellent ability to capture complex temporal patterns in the data. Rigorous multi-stage testing confirmed the stability and high generalizability of this deep learning model. The highly accurate forecast was incorporated into a mixed integer linear programming (MILP)-based model for home energy management system (HEMS) optimization. The results indicated that this complex framework significantly reduced energy costs by 28.7% and reduced peak load by 37.1% through optimal appliance scheduling. This work demonstrates how to effectively combine state-of-the-art artificial intelligence (AI)-based forecasting with formal energy optimization in a single, comprehensive system. This method not only allows for more accurate consumption forecasting, especially during peak hours, but also demonstrates that AI can significantly improve the flexibility of energy networks and the energy efficiency of smart homes.

Healthcare systems increasingly depend on the structured exchange of information between hospitals, laboratories, and digital platforms. The HL7 v2.x standard provides the backbone for this communication but remains challenging for machine interpretation because of its variable syntax and optional segments. To address this limitation, a hybrid artificial intelligence model was developed for automated processing and classification of HL7 messages, integrating both structural learning and semantic validation. The experimental workflow included the generation of a synthetic dataset of 3,000 patient lifecycles with more than 7,000 ADT messages, followed by parsing, feature engineering, and supervised training. Logistic Regression, Random Forest, and Gradient Boosting were evaluated as baseline classifiers, while a semantic layer combining Named Entity Recognition and Regular Expressions introduced context-aware features such as physician names, medical facilities, and diagnosis indicators. After retraining, ensemble models demonstrated measurable improvement, with Random Forest achieving an increase of +9.3 % in accuracy and +7.0 % in F1-score. The results confirm that the addition of semantic cues enhances model interpretability and overall robustness, bridging the gap between structured message parsing and naturallanguage understanding. The proposed hybrid pipeline may serve as a foundation for intelligent interoperability solutions and future FHIR-compatible healthcare data systems.

Real-time streaming systems face persistent memory pressure as continuous data ingestion drives unbounded growth in hot-tier storage. Existing Information Lifecycle Management (ILM) frameworks have been applied primarily to enterprise archival contexts and have not been evaluated within containerized streaming pipelines that employ multi-tier in-memory architectures. This paper presents a lightweight, policy-driven ILM mechanism integrated into a Kafka–Apache Flink pipeline with a three-tier storage model comprising MongoDB (hot-tier), TimescaleDB (warm-tier), and Parquet files (cold-tier). An asynchronous sweeper thread migrates records between tiers according to configurable time thresholds and , preventing hot-tier saturation without disrupting stream processing. Five experiments were conducted to evaluate memory efficiency, tier retrieval latency, threshold sensitivity, scalability, and extended lifecycle behavior. The results demonstrate that ILM reduces MongoDB peak memory usage by 81% (from 106.96 ± 1.63 MB to 20.28 ± 1.81 MB, p < 0.001) while Flink throughput and processing latency remain unaffected. Memory bounds hold stably across ingestion rates from 200 to 1,000 messages per second. An extended 90-minute run validates correct three-tier lifecycle operation, with MongoDB remaining bounded, TimescaleDB absorbing 2.28 million warm records, and Parquet accumulating cold archives. These findings confirm that effective, low-overhead ILM can be achieved in containerized real-time pipelines using only native database capabilities and file system operations.

The article presents an annotated corpus consisting of clinical texts in Russian, obtained by exomic sequencing. This corpus was developed to support the tasks of automatically identifying named objects and semantic relationships in relation to genes, mutations, hereditary diseases, phenotypic traits and their clinical significance. During the formation of the corpus, reports of actual clinical exomic sequencing were used, the data went through the stages of preliminary anonymization and text normalization. The labeling process used international standards and knowledge bases such as HGVS, OMIM, ClinVar, and HPO, and ensured consistency and accuracy of biomedical information. The corpus contains more than 25,000 biomedical objects and more than 6,000 semantic links, making it an important resource in the field of clinical genetics in terms of volume and content. The annotation was carried out manually with the participation of several experts, and the results were compared by cross-checking, and the level of agreement between the annotators was assessed using special indicators. The results obtained indicate the high quality and reliability of the case. The finished corpus makes it possible to effectively use natural language processing models in the field of medical genetics for teaching and evaluation, development of clinical decision support systems, and applied research for structuring genetic data.

Nowadays, the creation of smart manufacturing systems has high importance. Neural networks have been widely applied to solve complex manufacturing challenges. The paper is devoted to the study of neural networks with reinforcement learning as PPO (Proximal Policy Optimization), DQN (Deep Q-network) for state diagnosis of industrial equipment within the GEMMA (Guide d’Etude des Modes de Marche er d’Arret) model. The GEMMA French approach is established on the SFC (Sequential Function Charts) language and includes standards for controlling technical processes. An application of neural networks in area D of the GEMMA model is introduced. Modelling and experimental results were conducted based on synthetic and experimental datasets. The implementation of the architecture considered allows us to achieve reliable results for industrial data.

At present, in the field of medicine, high-quality and accurate translation from English into Kazakh is one of the key challenges in ensuring the accessibility and safety of medical information. This scientific study investigates the accuracy and effectiveness of machine translation systems widely used in practice, such as Google Translate and Yandex Translate, when applied to medical texts. The main objective of the study is to explore methods for achieving semantically and stylistically correct translation of medical terminology and complex sentences from English into Kazakh. For this purpose, a specialized corpus consisting of 102,374 sentences was compiled from international medical articles, clinical studies, and drug descriptions. The corpus was processed using the MarianNMT neural machine translation system and translated into Kazakh. For light post-editing of the translation results, the transformer-based Kaz-RoBERTa model was employed, while full post-editing was carried out using one of the large language models (LLMs), namely GPT-4.1, whose adaptability to medical texts was also examined. Translation quality was evaluated using the BLEU, TER, and METEOR metrics. The translations obtained after the initial MarianNMT machine translation were compared with the results after post-editing using the Kaz-RoBERTa and GPT-4.1 models. The analysis showed that translations processed with the Kaz-RoBERTa model achieved an 9% improvement over the baseline MarianNMT translations, while the use of the GPT-4.1 model resulted in a 23% improvement.

Buildings account for a significant portion of global energy consumption, with HVAC and humidity-control systems representing the majority of their operational demand. Traditional rule-based strategies often fail to adapt to dynamic indoor-outdoor conditions, motivating the use of data-driven control methods. This study presents a multiagent reinforcement learning (MARL) framework for simultaneous temperature and humidity control in a singlezone building modeled in EnergyPlus. The proposed approach employs the distributed Importance Weighted ActorLearner Architecture (IMPALA) algorithm with centralized training and decentralized execution (CTDE), enabling two agents: temperature and humidity to learn coordinated policies directly from high-fidelity simulation feedback. The results demonstrate strong learning performance: both agents improved their per-step rewards substantially (temperature +18.9%, humidity +33.7%), indicating effective convergence and cooperative behavior. The learned controller maintained thermal comfort comparable to the rule-based baseline (mean occupied temperature difference ≈ 0.04 °C; occupied PMV ≈ 0.45) while achieving notable energy savings. Total annual HVAC energy consumption decreased by 8.9%, with the most significant improvement observed in humidification energy, which was reduced by 34.4%. Heating and cooling loads remained nearly unchanged, confirming that energy reductions were achieved without compromising comfort.

Generative Artificial Intelligence (AI) transforms financial technology (FinTech) by creating synthetic data, enhancing predictive analytics, and automating complex tasks. This paper addresses the limitations of traditional machine learning models in handling data scarcity and evolving fraud patterns in finance. We propose a novel hybrid framework that integrates Generative Adversarial Networks (GANs), Large Language Models (LLMs), and Variational Autoencoders (VAEs) to improve credit scoring, fraud detection, and financial document automation. Our method employs a Conditional Tabular GAN (CTGAN) for synthetic data generation to balance datasets, a VAE for anomaly detection in transactional data, and an LLM for generating interpretable reports and compliance documentation. Experimental results demonstrate that models trained on GAN-augmented data achieve an 8% increase in AUC for credit scoring and an 18% improvement in F1-score for fraud detection on imbalanced datasets. A dedicated compliance layer reduced demographic bias by 37%. The study confirms that a carefully designed generative AI framework can significantly enhance model performance, fairness, and operational efficiency in FinTech applications while addressing critical ethical and regulatory challenges.

Credit card fraud occurs most often in online purchases; therefore, it is crucial to employ better ways to find it to avoid financial loss. This paper discusses fraud detection by employing methods to generate synthetic data to improve detection models. We use the Kaggle credit card transaction dataset, implementing synthetic data generation using SMOTE as a way to balance the dataset, in which fraud cases comprise only 0.2% of cases, and perform feature engineering to better understand buying behavior. We experimented with five ML models–XGBoost, LightGBM, Random Forest, Neural Networks, and Logistic Regression; focusing on precision, recall, F1-score, and accuracy. The comparison indicates that XGBoost achieves its highest F1-score (82.57%) with good precision (93.75%) and recall (73.77%), indicating XGBoost can balance false positives and false negatives. Although all models performed with high accuracy (over 99.9%), this research focuses on highlighting precision and recall in fraud detection. The findings suggest that combining synthetic data with gradient-boosting algorithms can help fraud detection systems improve the security of online purchases.

Natural language processing (NLP) methods are widely used in search engines, decision-support systems, and many other intelligent applications. One of the essential yet technically demanding tasks in this area is the extraction of triple relations in the form “subject–predicate–object.” Such structures are the basis for knowledge graphs and reasoning, but for languages with limited annotated resources, like Kazakh, this task becomes especially difficult. In our work, we investigate how the use of synthetic data can partially compensate for the lack of linguistic resources. The experimental setup included the generation of additional training data, followed by the training and testing of a model based on the Cross-lingual Language Model – Robustly Optimized BERT Approach (XLMRoBERTa) for triple extraction. XLM-RoBERTa, an improved version of the Bidirectional Encoder Representations from Transformers (BERT) model, benefits from a larger training corpus and increased size. This architecture is effective in cross-linguistic transfer tasks without additional fine-tuning, even between languages with different writing systems. The results show an F1-score of 90.73%. This indicates that even relatively simple augmentation strategies, when combined with advanced models, may considerably improve model performance when working with low-resource languages. The study also suggests that the approach can be extended to other underrepresented languages and integrated into practical systems for information retrieval and knowledge management.

This article examines the pressing issue of developing a secure, specialized online platform for distance learning for children with special educational needs. The digitalization of education has opened up new opportunities for inclusion, but mainstream solutions often fail to address the specific needs of this category of students, creating digital, cognitive, and social barriers. The goal of the study is to develop a conceptual model of a secure and adaptive educational environment that comprehensively addresses accessibility, personalization, and cybersecurity. The project took into account the usability of children with various developmental disabilities, including sensory impairments, autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD), and based on this, the key principles of user interface and user experience (UI/UX) design were formulated. The proposed platform architecture includes an intelligent content and interface adaptation system, personalized learning paths, and secure communication modules with pre-moderation functions. Particular attention is paid to a multi-layered security system that ensures the protection of personal data, the prevention of cyberbullying, and access control. The article is of practical value to educational technology developers, educators, and administrators of educational institutions seeking to create an inclusive digital learning environment.

This paper examines service mesh technology as a dedicated infrastructure layer for managing interservice communication in microservice systems. The objective of the study is to analyze the architectural principles of network layer construction, estimate performance overhead, and demonstrate practical approaches for operationalization in Kubernetes clusters. It describes a model for separating the control plane and data plane, using the sidecar pattern for transparent traffic routing, implementing mTLS, and automatically collecting telemetry. Based on a literature review and open source documentation, a comparative analysis of three key implementations – Envoy, Istio, and Linkerd – is provided based on functionality, implementation complexity, and resource consumption. A practical example of an Istio configuration is provided, using Gateway, VirtualService, and DestinationRule manifests to manage HTTPS traffic and integrate with the Prometheus – Grafana – Jaeger – Kiali observability stack. It was shown that Service Mesh adds moderate overhead in terms of latency and resource consumption, but provides unified management of security and traffic between microservices. The obtained results allow us to formulate practical recommendations on the feasibility of using Service Mesh in enterprise microservice platforms, depending on the system scale and security requirements.

Rice is a cornerstone of food security in India, supporting millions of livelihoods and the national economy. However, erratic climate patterns are making paddy yields increasingly unpredictable. This study develops a machine learning framework for rice yield prediction in Udham Singh Nagar district, Uttarakhand, by integrating weather, soil, and crop data. Among baseline classifiers, CatBoost performed best with 80.85% accuracy and a ROC-AUC of 0.90. To further enhance performance, Optuna-tuned CatBoost, XGBoost, and LightGBM models were combined into hybrid ensembles. The Weighted Hard Voting classifier, giving higher weight to CatBoost ([3,1,1]), achieved the highest accuracy of 97.37%, followed by Stacking (95.6%) and Soft Voting ensembles (up to 96%). These results were supported by strong ROC-AUC scores. Overall, the study shows that carefully optimized ensemble models can significantly improve yield prediction accuracy, offering a practical tool for more precise and sustainable rice farming in climate-sensitive regions of India.

As Artificial Intelligence systems are increasingly deployed in safety-critical domains such as healthcare and finance, ensuring their trustworthiness and compliance is paramount. While Deep Neural Networks have received significant attention in formal verification, traditional models such as Decision Trees, often preferred for their interpretability, cannot inherently enforce constraints after training for fairness and stability. This paper presents a novel, comprehensive approach for the formal verification of Decision Tree classifiers using Satisfiability Modulo Theories (SMT). We propose a robust translation scheme that converts trained decision trees into logical constraints, enabling constraint inference that guarantees demographic parity and local robustness at prediction time. We implement this framework by using the z3 SMT solver and validate it on widely recognized fairness benchmarks, including UCI Adult, German Credit, and Loan Approval datasets. Experimental results demonstrate that our constrained model effectively eliminates demographic parity violations with a marginal accuracy trade-off of less than 0.2%. This approach transforms the SMT solver from a simple diagnostic tool into a provably fair inference engine suitable for regulated industries.

This study addresses the urgent need to analyze digital threats in everyday discourse by constructing a 1,000text annotated corpus from social media and news platforms covering military and geopolitical events. The purpose of the proposed study is to address the urgent need to analyze digital threats in everyday discourse by creating an annotated corpus of texts with elements of information operations. A multi-layered annotation scheme captures semantic actors and pragmatic features – including impact type, emotional tone, disinformation markers, and intent (e.g., provocation, intimidation). Annotation via Label Studio ensured flexibility, quality control, and context sensitivity, with inter-annotator reliability (Cohen’s Kappa = 0.82) confirming consistency. In pilot experiments, the Onto-IO-BERT model achieved an F1-score of 0.81, outperforming baseline classifiers. Practical utility was validated through analysis of real Telegram messages. The framework is tailored for studying military information operations within Kazakhstan’s Ministry of Internal Affairs and the created corps is a new resource for analyzing military information operations, filling a significant gap in the existing data set. The presented corpus contains texts in Kazakh, Russian and English. The corpus is openly accessible at: https://github.com/baiangali/multi_mil

a bioinspired approach based on a unified artificial immune system featuring neuroendocrine regulation of homeostasis during knowledge acquisition. To implement an intelligent distance learning system for the control of complex industrial automation objects – based on the proposed UAIS–NES technology – an integrated ontological model has been developed. This model consists of ontological sub-models for the immune, neural, and endocrine subsystems, serving as the foundation for a knowledge base designed to integrate cognitive technology into the educational process. Classes and subclasses have been defined, and their interactions analyzed, to facilitate a two-stage data processing workflow: specifically, the reduction of non-informative features and subsequent classification, both executed using bio-inspired algorithms. System quality assessment criteria have been established based on metrics categorized into mutually exclusive classes, distinguishing between favorable and unfavorable outcomes. Based on the results of data analysis utilizing this technology, a forecast is generated regarding the level of acquired professional engineering skills, the probability of successfully mastering the academic discipline, and the student’s degree of readiness to tackle complex technical management challenges – all presented in the form of a digital student competency profile.

This article explores the potential of using machine learning methods to predict students’ critical thinking skills based on academic and behavioral data. Although critical thinking is widely recognized as a critical skill in modern education, its direct assessment remains challenging. The aim of the study was to identify the relationship between critical thinking development and measurable factors such as academic achievement, participation in extracurricular activities, and course selection. A dataset of 500 anonymized student profiles was collected and processed to extract key characteristics. Three models were developed and evaluated using standard performance metrics: linear regression, decision tree, and random forest regressor. The random forest model demonstrated superior predictive performance, achieving an R² value of 0.84, compared to 0.65 for the decision tree and 0.37 for the linear regression. The study’s findings demonstrate that students’ academic achievement, course selection, and level of extracurricular activity collectively provide valuable insight into their critical thinking abilities. Overall, the results support the effectiveness of data-driven approaches for indirectly assessing cognitive skills, which has practical implications for curriculum development, early intervention initiatives, and educational policy planning. By leveraging readily available educational data, this approach facilitates the development of more scalable, objective, and personalized assessment methods in the field of learning analytics.

This work presents an automated control system for the synthesis of nanomaterials by plasma-enhanced chemical vapor deposition (PECVD), implemented using the LabVIEW software environment. The main objective of the study is to develop an integrated hardware-software platform that enables sequential control of the key stages of the PECVD process, including vacuum chamber preparation, pressure monitoring, working gas supply, plasma ignition, power matching, cyclic nanomaterial growth, and optical monitoring of nanoparticles in the plasma environment. The use of LabVIEW made it possible to integrate actuator control, experimental parameter acquisition, and realtime process visualization within a single automated system. The automated cycle begins with evacuation of the reaction chamber to a predefined base pressure. Transition to the next stage is permitted only after the specified pressure threshold has been reached, ensuring reproducible initial conditions for each experiment. The program then controls the supply of the working gas through mass flow controllers (MFCs). In this work, two gas-flow control modes were considered: analog control using a 0÷5 V voltage signal and digital communication via RS-232 interface. It was shown that the analog approach requires accurate scaling of the control voltage, since applying 5 V corresponds to full-scale opening of the controller and results in the maximum gas flow. In contrast, the RS232 interface enables the gas flow rate to be specified directly in sccm, improving the accuracy, flexibility, and convenience of gas-environment control. After pressure stabilization, LabVIEW initiates RF plasma ignition and executes the RF matching algorithm aimed at minimizing reflected power and improving the stability of the plasma process. A separate software module implements the cyclic nanomaterial growth mode, in which the plasma-on time, plasma duration, and total number of synthesis cycles are predefined. This approach makes it possible to control material accumulation on the substrate and to correlate the process parameters with the morphological characteristics of the resulting nanostructures. The final module of the system is designed for optical monitoring of the nanoparticle cloud density in dusty plasma. For this purpose, the change in the intensity of laser radiation passing through the plasma region is recorded using a photodetector and a Keithley 2401 measuring unit connected to LabVIEW via RS-232 interface. The difference between the initial and modified optical signal intensity is used as a diagnostic parameter characterizing the formation and temporal evolution of nanoparticles. The developed system demonstrates that LabVIEW can be effectively applied not only for the automation of individual instruments, but also for the implementation of a complete digital control cycle for PECVD-based nanomaterial synthesis.

The process of sulfurization of nickel foam surfaces to obtain Ni₃S₂ layers with high electrochemical capacitance and stability during electrochemical cycling has been extensively studied. However, the role of nickel hydroxide layers, which are expected to form under the electrochemical operating conditions of the Ni₃S₂/NF electrode, has not been sufficiently investigated. In the present work, it is demonstrated that the hydroxide phase makes a significant contribution to both electrochemical capacitance and cyclic stability. The Ni₃S₂/NF electrode was fabricated via a single–step hydrothermal method in the presence of thiourea at 160 °C. The initial structure of Ni₃S₂ on the NF surface was subsequently modified through electrochemical cycling in a KOH electrolyte. The increase in electrochemical capacitance of the electrode was accompanied by the formation of multiple nickel hydroxide phases, as identified by X–ray diffraction (XRD) and Raman spectroscopy. The electrode exhibited high performance stability over 20,000 galvanostatic charge–discharge (GCD) cycles at a current density of 20 A g⁻¹, retaining 90% of its maximum capacitance. The specific capacitance of the Ni₂S₃ electrode was 758 F g⁻¹ at a current density of 2.7 A g⁻¹. When the current density increased to 90 A g⁻¹, the specific capacitance decreased to 233 F g⁻¹, corresponding to 30% of the capacitance at 2.7 A g⁻¹.

Aqueous zinc-ion batteries (AZIBs) are considered as one of the most attractive candidates for safe, low-cost, and environmentally benign energy storage systems. However, the widespread implementation of these systems is still limited due to major problems with the zinc metal anode, including uncontrolled dendrite formation, hydrogen evolution, and the low reversibility of zinc plating and stripping. These issues lead to rapid capacity fading and shortened cycle life, highlighting the urgent need for electrolyte optimization as a simple and effective strategy to overcome anode instability. The purpose of this work was systematically investigated the electrochemical behavior of zinc anodes in a series of electrolytes with different ZnSO₄-Li₂SO₄ compositions, namely 2M ZnSO₄, 1.5M ZnSO₄ + 0.5M Li2SO₄, 0.5M ZnSO₄ + 1.5M Li₂SO₄, 1M ZnSO₄ + 1M Li₂SO₄, and 2M Li₂SO₄. The electrochemical performance was evaluated using cyclic voltammetry (CV) and galvanostatic charge-discharge tests, and then post-cycle morphological characterization was performed using scanning electron microscopy (SEM). The results show that adding Li₂SO₄ to the ZnSO₄ electrolyte significantly changes the structure of the Zn₂+ solution, thereby increasing the reversibility of zinc plating/stripping and suppressing dendrite formation. In particular, mixed electrolytes exhibit sharper voltage profiles and reduced polarization compared to single-salt systems. Among the tested formulations, the equimolar mixture of 1M ZnSO₄ + 1M Li₂SO₄ achieved the most balanced performance, delivering stable cycling and a uniform zinc dendrite morphology. This study highlights electrolyte engineering as a practical and scalable approach to stabilize zinc anodes, providing new insights into the design of high-performance aqueous zinc batteries for future large-scale energy storage applications.

In the present study, the coupled behavior of thermal-hydraulic processes and solid mechanics phenomena was investigated using the finite element method implemented within the COMSOL Multiphysics environment. Neutronic analyses were performed using the OpenMC code under the steady-state neutron transport approximation. To optimize the core configuration of a thorium-fueled pressurized water reactor (PWR), high-pressure light water was selected as the coolant and moderator. The reactor core was composed of 49 fuel rods containing thorium-based fuel compounds arranged in a regular lattice configuration. Monte Carlo neutron transport simulations conducted with OpenMC enabled the evaluation of the effective neutron multiplication factor (k_eff) and the spatial power distribution within the reactor core. Thermal-hydraulic calculations yielded detailed temperature and coolant velocity distributions, while solid mechanics analyses provided the corresponding spatial distributions of stresses and deformations arising under operational conditions. The obtained results demonstrate the applicability and effectiveness of a coupled multiphysics approach integrating steady-state neutronics with thermal-hydraulic and structural analyses for the assessment and optimization of thorium-fueled pressurized water reactor systems.

This paper examines the economic efficiency of Blast Movement Monitors (BMMs) in a grade control environment within open-pit mining operations. Blast-induced displacement of ore-waste contacts remain a major constraint on effective grade control in open-pit mining. Unaccounted movement during blasting frequently leads to ore dilution, loss of valuable material, and misclassification, ultimately reducing processing efficiency and economic returns. The study is based on the premise that blast-induced rock mass movement is inherently stochastic and cannot be reliably reproduced using deterministic modelling alone; therefore, high-precision grade control requires direct in-situ measurement. By capturing three-dimensional displacement vectors from multiple monitoring points within a blast block, BMM enables more accurate reconstruction of post-blast ore boundaries. This multi-point approach accounts for spatial variability and provides a robust basis for updating polygons prior to excavation. In 2024, the pilot implementation of the BMM system was started at the Zhairem Mining and Concentrating Complex (MCC), and the system is being implemented at several Kazakhstan mining companies. Using field data from the Zhairem site, the study quantifies the operational consequences of uncontrolled blast movement, including ore loss, dilution, and misclassification. The results indicate that improved boundary delineation enhances resource utilisation and contributes directly to increased profitability. Based on these findings, the paper proposes a financial value assessment tool designed to support investment decisions related to blast movement monitoring systems, enabling operations to systematically evaluate the cost–benefit ratio of BMM implementation and optimise grade control strategies within a broader economic framework.

This study presents an unsupervised workflow for delineating prospectivity zones for stratiform copper-cobalt mineralization in the southern margin of the Central African Copperbelt, Zambia, using only airborne geophysical data (gravity, magnetics, radiometry, terrain). Three clustering algorithms-K-Means, Fuzzy C-Means (FCM), and Self-Organizing Maps (SOM)-were applied, followed by consensus clustering to isolate robust target zones. Key geophysical filters (tilt derivative, total horizontal derivative, analytic signal) and radiometric ratios (U/Th, U/K) were computed and compared across clusters. The most prospective cluster was validated against literature-calibrated geophysical thresholds and was found to match known exploration criteria and regional structural trends. A final probabilistic prospectivity map was generated from FCM membership, classifying targets into three confidence levels. While lack of drill-hole data prevents quantitative accuracy assessment, this approach demonstrates that fully unlabeled machine learning on airborne data can effectively guide early-stage exploration in data-sparse regions. The proposed workflow offers a reproducible framework for AI-driven prospectivity mapping in frontier terrains.

In deep reservoirs, changes in pore pressure during field development often lead to a situation where the difference between overburden pressure and pore pressure reaches values sufficient to cause deformation of the rock skeleton. This results in a reduction in both the number and diameter of open pores, leading to significant changes in the storage capacity and filtration properties of reservoir rocks. Under such conditions, the nature of fluid inflow to horizontal wells is expected to differ significantly from that observed in reservoirs located at shallow depths. In this regard, there is a growing need to develop models of filtration processes for deep reservoirs composed of fractured and deformable formations developed by horizontal wells. Therefore, conducting research aimed at developing methods for determining the productivity of horizontal wells operating in deformable reservoirs has become an important scientific and practical task. This article is devoted to improving the methodology and analyzing hydrodynamic studies of fluid filtration processes in deformable homogeneous and homogeneous-anisotropic reservoirs during their exploitation by horizontal and vertical wells. One of the largest oil fields in Kazakhstan – field Z, which has long ensured the required levels of oil production, is entering the final stage of development. In recent years, the number of fields with hard-to-recover residual oil reserves in gas-cap zones, low-permeability reservoirs, as well as in formations containing highly viscous oils complicated by faults and active bottom waters has significantly increased. The effective development of such reservoirs cannot be ensured using conventional well operation technologies. Currently, the development of oil and gas fields using horizontal wells is one of the most promising directions of scientific and technological progress aimed at involving hard-to-recover oil and gas reserves in industrial production.

A comprehensive study has identified the principal geological and mineralogical controls governing the formation of iron skarn deposits in the Northwestern Balkhash region, based on representative deposits such as Zhuantobe and Karaulken. Integrated analysis of geological mapping, mineralogical and petrographic observations, geochemical data, and geophysical interpretations demonstrates that skarn formation represents a multistage contact-metasomatic system controlled by magmatic, structural, and fluid-related factors. Systematic patterns in the distribution of mineral assemblages and ore textural-structural types have been established, reflecting variations in fluid regimes and physicochemical conditions of ore formation. The obtained results refine the regional genetic model of iron skarn formation in the Northwestern Balkhash region and can be applied to the prediction and evaluation of the exploration potential of new ore targets.

The aim of this study is to determine the phase forms of gold in the oxidation zone of the Arkharly deposit and to assess their influence on the efficiency of gold and silver recovery. The main research methods included mineralogical analysis, electron microprobe studies, and technological testing. The study identified and characterized silver halides, established their genetic relationship with gold, and substantiated the role of supergene processes in the redistribution of gold. The Arkharly gold-silver deposit (Kazakhstan) is characterized by a complex composition of ores and a variety of gold occurrence forms, which significantly affect processing efficiency. This paper examines the phase forms of gold in the oxidation zone and their impact on technological recovery indicators. Based on mineralogical, electron microprobe, and technological studies, it was found that gold occurs in various forms: native gold of different fineness, electrum, as well as finely dispersed particles associated with silver minerals. Particular attention is given to silver halides, widely developed in the oxidation zone and forming complex intergrowths with native silver and gold. Textural and structural features of mineral aggregates indicate the redistribution of gold under supergene conditions. The formation of gold-bearing phases is associated with chloride and, presumably, colloidal migration of matter, leading to the formation of zonal structures and inclusions of high-fineness gold. Technological test results showed low efficiency of gravity concentration due to the fine-dispersed nature of gold, and high efficiency of cyanidation and sorption leaching (gold recovery up to 93.5%). A direct relationship between the phase forms of gold and recovery indicators has been established. The obtained results expand the understanding of gold behavior in the oxidation zone and can be used in developing effective technologies for processing complex gold-silver ores.

Skarn iron ore deposits represent a significant component of the global iron resource base, yet their development is commonly associated with substantial environmental disturbance due to large-scale open-pit mining. This study presents an integrated geological and geoecological assessment of the Zhuantobe skarn iron ore deposit (Central Kazakhstan), focusing on the relationship between ore-forming processes and environmental response under active mining conditions. The research combines geological, mineralogical, geochemical, geophysical, and long-term environmental monitoring datasets (2018–2025). Ore bodies are characterized by complex morphology controlled by fault systems and multistage skarn evolution, including prograde (garnet–pyroxene) and retrograde (magnetite–sulfide) assemblages. Environmental monitoring indicates a systematic reduction in anthropogenic impact, including a decrease in dust load (>40%), soil iron content (~40%), and CO₂ emissions (~35%). The results demonstrate that the implementation of integrated environmental protection measures effectively stabilizes environmental parameters within permissible limits. The study provides a framework linking geological structure with environmental performance and proposes a set of transferable strategies for sustainable development of skarn iron ore deposits.

Project management has primarily developed within high-income countries. However, over the last 15 years, capital-intensive projects have been shifting toward developing nations where conventional frameworks often underperform. Despite this, research on the critical success factors (CSFs) and risks influencing project outcomes in developing countries remains fragmented. This paper conducts a systematic literature review of 41 peer-reviewed articles published in the Scopus database in the last decade, covering 20 developing countries and 8 cross-country studies. Following the PRISMA protocol, this study identifies CSFs and risks in project management (PM) in developing countries through inductive review and deductive coding based on two theoretical lenses, namely, institutional and contingency theory. Furthermore, this paper analyzes the mechanisms and contextual dependency of these factors. The inductive findings identify 22 CSFs and 21 risk factors. In order to report the most critical ones, 8 CSFs and 8 risks are retained based on their frequency of occurrence. The results reveal that they form systematic corresponding pairs, with six of the eight CSFs directly mapping onto six risks. Moreover, institutional theory (IT) explains the dominance of external influences through coercive, normative, and mimetic mechanisms, as well as institutional voids, while the contingency theory (CT) shows variable outcomes across different project characteristics. Overall, the findings provide practical implications for project managers, policy makers and business organizations on managing projects in emerging countries. It is noted that project strategies should be adapted to project scale, with stakeholder-focused approaches in small projects, institutional building in medium projects, and macroeconomic and political risk management in large projects.

This study examines the relationship between project-level characteristics and total investment in greenfield infrastructure projects. While existing foreign direct investment literature primarily focuses on macroeconomic and institutional determinants, project-specific characteristics remain comparatively underexplored. Using a dataset of 131 infrastructure projects obtained from the World Bank Private Participation in Infrastructure (PPI) database, the study applies multiple linear regression analysis to evaluate the effects of physical assets, project capacity, procurement conditions, financial structure, and country-level characteristics on investment size. The results show that physical assets have a positive and statistically significant effect on total investment, confirming the capitalintensive nature of greenfield infrastructure development. In contrast, project capacity demonstrates a significant negative relationship, suggesting the presence of operational efficiency or economies of scale. Other variables, including procurement competition, financial structure, and country-level conditions, do not demonstrate statistically significant effects. The findings provide empirical evidence that investment size in greenfield infrastructure projects is influenced more strongly by internal project characteristics than by broader external conditions. The study contributes to project-level infrastructure investment literature and highlights the importance of technical planning, infrastructure configuration, and operational assessment in investment decision-making.

This article examines the drivers and barriers to implementing Environmental, Social and Governance (ESG) principles in organisational and project-related management practices in emerging countries. A systematic literature review based on the Scopus database was conducted using PRISMA logic and interpreted through institutional theory. The search identified 605 records, from which 40 peer-reviewed studies were included after screening and eligibility assessment. The findings show that ESG implementation is shaped by the interaction of regulatory pressure, organisational readiness, financial feasibility, stakeholder coordination and implementation capability. The main drivers include supportive regulation, stakeholder pressure and involvement, training, leadership commitment, governance arrangements and technological capability. The main barriers include high upfront costs, restricted finance, fragmented regulation, insufficient professional competence, low awareness, cultural resistance and weak stakeholder engagement. The results reveal an imbalanced distribution of attention across ESG dimensions. Governance emerges as the most visible, appearing in 62.5% of the reviewed sources. Environmental follows with 55.0%. Social receives the least attention, at only 32.5%. The article identifies a persistent translation failure between ESG adoption and its operational implementation across organisational and project-related contexts. The findings contribute to understanding ESG implementation as an institutionally conditioned and capability-dependent process and offer implications for project managers, organisations and policymakers.

Project management has primarily developed within high-income countries. However, over the last 15 years, capital-intensive projects have been shifting toward developing nations where conventional frameworks often underperform. Despite this, research on the critical success factors (CSFs) and risks influencing project outcomes in developing countries remains fragmented. This paper conducts a systematic literature review of 41 peer-reviewed articles published in the Scopus database in the last decade, covering 20 developing countries and 8 cross-country studies. Following the PRISMA protocol, this study identifies CSFs and risks in project management (PM) in developing countries through inductive review and deductive coding based on two theoretical lenses, namely, institutional and contingency theory. Furthermore, this paper analyzes the mechanisms and contextual dependency of these factors. The inductive findings identify 22 CSFs and 21 risk factors. In order to report the most critical ones, 8 CSFs and 8 risks are retained based on their frequency of occurrence. The results reveal that they form systematic corresponding pairs, with six of the eight CSFs directly mapping onto six risks. Moreover, institutional theory (IT) explains the dominance of external influences through coercive, normative, and mimetic mechanisms, as well as institutional voids, while the contingency theory (CT) shows variable outcomes across different project characteristics. Overall, the findings provide practical implications for project managers, policy makers and business organizations on managing projects in emerging countries. It is noted that project strategies should be adapted to project scale, with stakeholder-focused approaches in small projects, institutional building in medium projects, and macroeconomic and political risk management in large projects.

Sustainable finance (SF) has emerged as a pivotal tool in addressing global and societal challenges by integrating economic, social, and governance factors into investment and decision-making processes. In Hyderabad, a rapidly growing urban hub with a significant youth population, Generation Z investors are increasingly influencing the sustainable finance landscape. This study explores the awareness, motivations, and investment behaviours of Gen Z in Hyderabad, with a particular focus on their engagement through social media platforms. Using Structural Equation Modelling (SEM) via SmartPLS, primary data were collected from 152 Generation Z respondents through a structured survey. The study finds that sustainable finance attitude is the strongest predictor of investment behaviour (β = 0.5583, p = 0.0092), followed by technology awareness (β = 0.4125, p = 0.0486), while financial literacy alone is not a statistically significant predictor (β = 0.3087, p = 0.1253). These findings challenge the conventional assumption that financial literacy drives sustainable investment participation, positioning technology accessibility and values alignment as more powerful behavioural enablers. The research highlights the transformative potential of Generation Z in reshaping Hyderabad into a model city for sustainable finance in India, offering actionable insights for policymakers, financial institutions, and educators.