Engineered Science

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2576-988X

2576-9898

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Integration of Mathematical Modeling and Artificial Intelligence to Predict Thermal Conditions and Optimize the Operation of Biogas Plants
(2025-01-01)
Omirlan Auelbekov
;
Айнур Козбакова
;
Kairat Yessentayev
;
Timur Merembayev
;
Kuanyshbek Igibayev
Modern biogas plants face a number of challenges related to precise control of thermal conditions, which significantly affect the stability of processes and the overall efficiency of energy production.The complexity lies in the nonlinear and dynamic nature of biochemical reactions occurring under anaerobic conditions, as well as in a large number of factors affecting heat transfer inside the reactor.In this regard, there is a need to develop adaptive and highly accurate models capable of predicting thermal processes and optimizing the operation of biogas systems.In this study, we propose a hybrid modeling framework that integrates physics-based mathematical models with machine learning algorithms, enabling real-time prediction and adaptive control of thermal processes.Unlike prior models focused solely on thermodynamics or empirical
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Nanomaterial-Enabled Electrochemical Biosensors for Highly Sensitive Detection of Cancer Biomarkers: Fundamentals, Advances and Future Perspectives
(2026-01-01)
Ainur Yerezhepova
;
Zhazira S. Mukatayeva
;
Yrysgul Bakytkarim
;
Nurbala Myrzakhmetova
;
Nurgul Shadin
Early and accurate detection of cancer biomarkers is essential for improving patient survival and advancing precision medicine. Electrochemical biosensors have emerged as promising diagnostic platforms due to their high sensitivity, rapid response, low cost, and compatibility with miniaturized and point-of-care systems. Recent advances in nanomaterials have significantly enhanced sensor performance by improving electrode conductivity, increasing electroactive surface area, enabling efficient bioreceptor immobilization, and facilitating signal amplification. This review presents a comprehensive and critical overview of nanomaterial-enabled electrochemical biosensors for cancer diagnostics. The fundamental principles of electrochemical biosensing and key transduction modes—voltammetric, amperometric, impedimetric, potentiometric, and photoelectrochemical—are systematically discussed. Particular attention is given to clinically relevant biomarkers, including proteins, nucleic acids, exosomes, metabolites, and circulating tumor cells, as well as their detection challenges in complex biological matrices. Recent progress in signal amplification strategies is highlighted, including nanomaterial-based interfaces, catalytic and enzyme-assisted systems, redox probes, and nucleic acid amplification techniques, enabling ultra-low detection limits. Key performance parameters, multiplexing approaches, and integration with liquid biopsy are also addressed. Current challenges such as biofouling, interfacial instability, reproducibility, and scalability are critically evaluated. Future directions focus on advanced nanomaterial design, robust interfaces, scalable fabrication, and integration with microfluidics, wearable systems, and digital analytics.
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Characteristics of Cyclic Freeze-Thaw of a Highway Pavement and Subgrade
(2026-01-01)
Bagdat Burkhanbaiuly Teltayev
;
Giuseppe Loprencipe
;
Yerbol Aitbayev
;
Azamat Zhaisanbayev
;
G. I. Salgarayeva
Temperature has a strong influence on the mechanical behavior of road pavement layer materials and subgrade soil which requires reliable information on temperature changes in them.At different characteristic periods of a year temperature can be one of main factors in a destruction of structural elements of highways.This paper analyzes the results of monitoring of temperature and moisture in layers of a pavement structure and its subgrade on the section (58 km + 895 m) of the "Almaty-Bishkek" highway.The monitoring was carried out using a special measuring system.In particular, based on the measured temperature values, characteristics of cyclic freeze-thaw (number, durations and minimum temperatures) in the first asphalt concrete layer of the pavement are established: the number of FTCs is 83; most FTCs have a duration of no more than 20 hours (80.72%) and a minimum temperature of no lower than -6°C (more than 80%); there are correlations between duration and minimum temperature for short (less than 25 hours) and (more than 25 hours) long durations.Correct accounting of the characteristics of FTCs will increase the strength and durability of road pavements.
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Humic Acid Modified Applied Palladium Catalysts for Nitro Compounds Reduction
(2023-01-01)
Индира Мухаметкеримовна Джелдыбаева
;
Zhaksyntay K. Kairbekov
;
Manshuk Z. Esenalieva
;
Altay Zh. Kairbekov
;
S.M. Suimbayeva
This work studies the possibility of using humic acids (HA) as a natural polymer-modifier for applied palladium catalysts.New catalysts based on palladium -HA fixed on the carrier bauxite-094 (B-094) have been synthesized and their catalytic properties in the process of reducing p-nitrodiethylaniline (PNDA) and potassium o-nitrophinolate (PtONPh) have been studied.Humic acid is a non-toxic, inexpensive, easily accessible high molecular weight polymer.Therefore, its use as a modifier polymer for applied palladium catalysts in the processes of hydrogenation of nitro compounds is very cost-effective and relevant as of today.This work presents the results of studies on different methods for the preparation of polymer metal catalysts.It has been shown that HA has stronger acid and sorption properties, greater branching and asymmetry of macromolecules.It has been established that the activity and stability of polymer-metallic complexes (PMC) based catalysts depend on the sequence of application of the active phase on the carrier.The largest specific surface area is characterized by the catalyst 1.0% Pd-HA (1.0%)/B-094, which exhibits the greatest activity and stability in the reduction reactions of PNDA and PtONPh.Considering that HA (polymer) and bauxite-094 (carrier) are materials of natural origin, the development of such catalysts is not accompanied by large economic costs, which is one of the main requirements for modern catalysts.
Цитирования (OpenAlex) 4Цитирования (OpenCitations) 3
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Antioxidant Activity and Bioavailability of Humic Substances of Low-Mineralized Sulphide Mud
(2023-01-01)
Ж. К. Каирбеков
;
S.M. Suimbayeva
;
Индира Мухаметкеримовна Джелдыбаева
;
Manshuk Z. Esenalieva
;
А.Ж. Каирбеков
Humic substances (HS) are natural compounds, the formation of which occurs in natural environments as a result of chemical and biological decomposition.HS accounts for 60% to 80% of organic matter in soil and aquatic environment.Therefore, we obtained samples of HS isolated from low-mineralized sulphide mud deposits of Tuzkol Lake (Republic of Kazakhstan) were obtained.HS samples have been characterized by instrumental methods: EPR spectroscopy and amperometry.Analysis of the concentration of paramagnetic centers shows that the minimum number of free radicals contains molecules of fulvic acids, and the maximum number of free radicals contains molecules of humic acids (HA) of peloids.The antioxidant activity and bioavailability of humic preparations in the form of sodium humates depending on the concentration of HS have been investigated.The information obtained indicates that HS have antioxidant activity and bioavailability, the value of which increases when the concentration of the solution grows.Dialysis of silver humate has been carried out in the presence of dimethyl sulfoxide (DMSO) at concentrations of 0.1; 1.0; 5.0 and 10% w/w.For HA and silver humate, a 10% concentration of DMSO is optimal, which allows a maximum yield of peloid preparations and a 1.6-fold increase in permeability.Dialysis of silver humate is 1.2 times more intensive than dialysis of HA.It has been shown that the change in the total content of antioxidants in various fractions of humic substances increases in the series; fulvic -humic -gimatomelanic acids.
Цитирования (OpenAlex) 1
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Research of the Finite Difference Numerical Method Using Artificial Intelligence for Solving Problems of Incompressible Fluid Flow in a Rectangular Domain with Initial and Boundary Conditions
(2025-01-01)
Lyailya Tukenova
;
Omirlan Auelbekov
;
S. Sapakova
;
Bayan Abduraimova
;
Ж. Р. Уалиев
This study explores the enhancement of the finite difference method using artificial intelligence (AI) to model incompressible fluid flow within a rectangular domain.Accurate and efficient modeling of incompressible flows is crucial in hydrodynamics and aerodynamics, where traditional numerical methods like finite difference often face limitations in accuracy and computational speed, especially for complex problems with varied initial and boundary conditions.By integrating AI techniques such as neural networks and machine learning, this approach enables adaptive parameter selection, optimizing the calculation process, improving accuracy, and reducing computational demands.The combined finite difference and AI approach demonstrated significantly reduced computational complexity and improved accuracy, as verified across multiple test cases with different boundary conditions.Results indicate that AI-enhanced numerical methods improve the efficiency and reliability of solutions for complex hydrodynamic problems, offering faster, more precise modeling of fluid flow behaviors.The study highlights the potential of AI to enhance classical numerical methods, enabling the development of more accurate fluid flow models even with limited computational resources.The novelty of this work lies in the specific integration of machine learning with the finite difference method, presenting an adaptive approach that extends beyond traditional techniques.This integration opens up new possibilities for handling complex fluid dynamics scenarios, expanding the applications of numerical methods in scientific and engineering calculations.

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