Purpose. Purpose is to generalize and analyze the currently accumulated geological and physical as well as field data to study productive strata of Prorva group of oil-and-gas occurrence of the Southern deposits of the Caspian Depression in Kazakhstan. Methodology. Deep Triassic rock systems were studied thoroughly relying upon the explained new data by seismic data together with logging and field information using current processing and interpretative systems according to the newly drilled deep wells within the unexplored northwest part of the structure. Findings. The specified geological model of the field has been obtained. Twenty-one estimation targets have been singled out in the Jurassic-Trias structures; three new oil-and-gas-bearing formations have been identified as part of the Lower Cretaceous share of a productive stratum within the Valanginian level. According to geophysical analysis, Western Prorva and S. Nurzhanov deposits are the unified system with the oil-water contact level. Originality. Selection of the recommended location of the new wells is based upon specificity of the innovative scientific and technical approaches while studying regularities of facies substitution of reservoir rocks with enclosing systems of each productive stratum; and studying permeability and porosity of the reservoir rocks as well as fluids saturating them.

A geological and geophysical database of hydrocarbon fields in the Kazakhstan part of the Caspian depression wasbuilt, enabling the efficient geological forecasts and assessment/re-assessment of field reserves. The database has the capacity to reuse and replenish the stored information, allowing the oil and gas companies, engaged in modelling, to effectively address issues of complex interpretation of geological and geophysical data for prospecting, exploration and evaluation, complex geological structure of oil and gas fields. The WEB-based Oil&Gas Resources Database Management System (OGR. Version 1.0) was developed, the pilot loading of initial information on 60 oil and gas fields into the database was carried out, and a basis for using this information in specialized geoinformation systems for building hydrocarbon reservoir models on complex geological and geophysical data was developed. This will enable subsoil users to apply information resources accumulated over many years more quickly and efficiently to prepare digital databases of geological and geophysical data needed to study and monitor the state of the subsoil geological structure, assess hydrocarbon reserves and conditions of their location in the subsoil.

Comprehensive interpretation of geophysical data for studies of the Earth crust deep horizons in the Caspian oil and gas bearing region is based on analysis of regional seismic section wave fields as per reference geotravers and gravity anomalies’ interpretation results received by posing direct and inverse problems with use of specialized geoinformation systems. Gravity exploration data in the form of consolidated medium- and small-scale maps and the observed travel time graphs of DSS and Reflection Wave/DSS refraction waves taken in separate regional profiles serve as the factual basis for construction of deep geological-geophysical sections. Physical models of density and structural-velocity proved to be effective and allowed identifying vertical and lateral heterogeneities in the crust and upper mantle structure. The models for upper horizons of the Earth crust are most reliable, as they are provided with a large amount of factual material, including geological data. Structural-velocity sections built on regional seismic profiles revealed heterogeneous structure of the North-Caspian region’s lithosphere. Negative and positive velocity anomalies characterize the lower horizon blocks of the Earth crust and the upper mantle of different material composition and structure. The gravity models of deep structures and density distribution in the Earth crust allowed to reveal the vertical and sub-vertical boundaries of deep blocks, to predict the material composition of deep structures and to determine the peculiarities of their formation and tectonic development. Given the poorly explored territory and incomplete geophysical information, the proposed method of the complex interpretation of seismic gravimetric data enables to construct reliable density and velocity models of complex geological situation. The obtained models of the region’s deep structure can be effectively used both for reconstruction of Paleozoic geodynamic conditions and identification of the modern structural features of the Earth crust and upper mantle.

Complex dynamic systems are characterized by the presence of memory effects and a multi-level hierarchy of relaxation times at various stages of transport processes. Therefore, reliable models of transport phenomena in such systems necessarily include a nonlocality factor due to the aftereffects of disturbances at different time stages of the process. At the same time, control parameters in models describing such dynamic systems can also change at different rates over different time intervals under the influence of external influences and relaxation processes that alter the system's structure. This factor is not always taken into account when constructing a process model. In this paper, a new heuristic model for accounting the impact of disturbances on the model structure and the appropriate control equation for describing the memory effects and the changes in the system's dynamic characteristics have been submitted. The novelty of the approach lies in the new concept for building the model, according to which the manifestation of after-effects can be caused by the memory effects formed, in turns, as a result of a change in the depth of potential wells corresponding to the stationary states of the system during the model process. Such an approach provides mathematical tools for studying bifurcation phenomena in a dynamic system described by a two-parameter model. The details of the new concept and the scheme for deriving the control equation are given. This article is theoretical in nature; the concept is based on general physical considerations. The results of conducted researches and the novel model will nevertheless can find application in engineering practice in the design of various technological processes. © 2026, World Scientific and Engineering Academy and Society. All rights reserved.

Mean Shift is a flexible, non-parametric clustering algorithm that identifies dense regions in data through gradient ascent on a kernel density estimate. Its ability to detect arbitrarily shaped clusters without requiring prior knowledge of the number of clusters makes it widely applicable across diverse domains. However, its quadratic computational complexity restricts its use on large or high-dimensional datasets. Numerous acceleration techniques, collectively referred to as Fast Mean Shift strategies, have been developed to address this limitation while preserving clustering quality. This paper presents a systematic theoretical analysis of these strategies, focusing on their computational impact, pairwise combinability, and mapping onto distinct stages of the Mean Shift pipeline. Acceleration methods are categorized into seed reduction, neighborhood search acceleration, adaptive bandwidth selection, kernel approximation, and parallelization, with their algorithmic roles examined in detail. A pairwise compatibility matrix is proposed to characterize synergistic and conflicting interactions among strategies. Building on this analysis, we introduce a decision framework for selecting suitable acceleration strategies based on dataset characteristics and computational constraints. This framework, together with the taxonomy, combinability analysis, and scenario-based recommendations, establishes a rigorous foundation for understanding and systematically applying Fast Mean Shift methods.

The work studies the processes of contact melting between a mixture of refractory metal powders, carbon nanofibers and substrates made of steel plates of different grades, which arise after applying a current pulse to this composition from a contact welding machine. It has been established that in the proposed system, contact melting and sintering of individual components can occur selectively. This allows us to propose the use of such an effect for forming parts in additive technologies. The addition of carbon nanofibers ensures that contact melting occurs with lower energy costs and between certain components with the formation of materials of the desired structure, grain size, physical and mechanical properties. Experimental studies have shown that due to the rational selection of the composition of the charge, substrate materials and parameters of electrical pulses from the contact welding machine, it is possible to ensure selective directional melting of components and the formation of the material of the part with the desired properties. The introduction of refractory metals (Cr, W, Mo, V, etc.) and nanofibers into the composition of the charge allows us to ensure provide the required hardness, chemical resistance and strength of the material. A small content of metals inactive to carbon (Cu, Ag, Au, Ga, In, Ge, Sn, Pb, Bi and Sb) in the charge significantly improves the wetting and solubility in melts of carbon materials.

Carbohydrate metabolism disorders increase the risk of developing active tuberculosis and are associated with worse outcomes of tuberculosis treatment. 15% of tuberculosis cases worldwide are estimated to be related to diabetes mellitus. Altered lipid metabolism may influence susceptibility to tuberculosis. Studies have been published on the association between total cholesterol, low-density lipoprotein, and high-density lipoprotein levels and the risk of tuberculosis. Patients with tuberculosis have an increased incidence of cardiovascular and cerebrovascular diseases. Cardiovascular complications of tuberculosis are among the most common extrapulmonary manifestations of the disease.

This study presents the analysis and modeling of the thermal regime of a furnace lining at an industrial copper smelting facility using a combined approach based on neural network (NN) technologies and the finite element method (FEM). Experimental temperature data were collected from a laboratory setup equipped with three thermocouples (TP-2488/1 and TCRosemount 0065), with a sampling frequency of 1 Hz over a total duration of 5 hours, resulting in 18,000 measurement points. The measurement uncertainty of the thermocouples did not exceed ±1.5 °C. These data were used both for model development and for validating the numerical FEM simulations. A feedforward neural network was trained using 70% of the dataset, while 15% and 15% were used for validation and testing, respectively. The prediction error of the neural network remained within 3% with a 95% confidence interval of [2.6%, 3.4%]. The results show that the proposed hybrid approach improves temperature prediction accuracy and reduces static control error by 15% when combined with a proportional–integral controller. The methodology demonstrates significant potential for improving thermal process stability and reducing energy consumption in high-temperature metallurgical systems. © 2026 Institute of Advanced Engineering and Science. All rights reserved.

In the mining industry, when breaking rocks, oversized pieces are formed that exceed the dimensions of the technological equipment. To carry out secondary rock fragmentation in quarries, it is proposed to use an electromagnetic hammer. At the D.A. Kunayev Institute of Mining, a prototype of a medium class electromagnetic hammer with an impact energy of up to 6000 J has been developed. The use of the electromagnetic hammer for breaking oversized pieces in the mining industry will increase the efficiency, environmental friendliness, and automation of the technological processes of rock and ore fragmentation. To develop a competitive prototype of a rock-breaking hammer, it is necessary to justify its dynamic parameters. A microprocessor-based measurement system was developed to measure the dynamic parameters of the prototype of the electromagnetic hammer, followed by the calculation of the energy characteristics of the prototype using the software “Matlab.” A digital sensor “HC-SR04” and a microprocessor controller “Arduino Mega 2560” were used to measure the height of the moving part of the electromagnetic hammer. To eliminate measurement errors, calculate the dynamics of the plunger’s velocity, acceleration and impact energy values, and plot the dependency graphs, the “Matlab” program version R2021b was used. Experiments were conducted by supplying the power voltage to the upper coil with a current value of 120 amperes, while the lower coils received the power voltage with current values of 70, 100, and 120 amperes. The data obtained, after processing in the “Matlab” software, allowed for the construction of dynamic parameter graphs for the prototype. The analysis of the graphs revealed the design flaws of this prototype, the elimination of which will improve the technological characteristics of new developments. © 2025, National Academy of Sciences of the Republic of Kazakhstan. All rights reserved.

The object of the study is an IEEE 802.15.4 (2.4 GHz) wireless networked control system (WNCS) closing the loop over a wireless sensor network. Fading and interference increase packet loss and delay, reducing stability margins and control quality. The unresolved problem is the lack of a unified end-to-end (E2E) loss model that links PHY signal quality, multi-hop routing and medium access to closed-loop behavior and can be embedded into controller synthesis. An SINR-based channel model (path loss, lognormal shadowing, multipath fading) is mapped to BER and packet error probability; E2E loss for single-hop and multi-hop routes is obtained using Bernoulli and finite-state Markov (FSMC) processes. For verification, original packet traces are captured with an IEEE 802.15.4 sniffer/logger and stored before processing (timestamp, node identifier, sequence number, RSSI/LQI and delivery outcome) to compute PER, latency and burstiness and to parameterize the SINR-to-PER mapping and loss models. Simulations show that TDMA/TSCH achieves up to 40% lower loss than CSMA/CA, while E2E loss rises from 3% to 32% as hop count increases from 1 to 8. An MPC-based co-design jointly adapts transmit power, sampling period and retransmissions. Compared with a fixed-parameter LQR baseline, E2E PER is reduced from 4.45% to 3.66%, average delay from 0.20 s to 0.12 s, and integral absolute error by 50%. The gains are attributed to reduced contention under TDMA scheduling and predictor-driven MPC adaptation. The approach targets industrial monitoring and control with fixed sampling, slowly varying interference and static multi-hop topologies, where parameters can be identified offline and used for online MPC adaptation Copyright