In this work, an amorphous silica powders (1-SiO2 and 2-SiO2) and activated carbon with few-layer graphene were derived from rice husk using simple and efficient process. Synthesized samples were characterized by means of TGA SEM, XRD, XRF, nitrogen low-temperature adsorption/desorption, Raman, and EDAX. Rice husk calcination yields in 18 and 14 wt% of 1-SiO2 and 2-SiO2, correspondingly. Carbonization of rice husk followed by activation yielded about 11 wt% of activated carbon with large specific surface area (SBET = 3292 m2 g−1). Feasibility of resulting materials as anode materials for Li-ion batteries grounded on a high theoretical capacity of silica and high charge/discharge stability of activated carbon. We present here a 2-SiO2 material with large specific surface area of 980 m2 g−1, of high purity above 99 %, and large pore volume (1.20 cm3 g−1), which results in a reversible capacity of about 841 and 442 mAh g−1 (1st and 50th cycle) with coulombic efficiency higher than 95 %. Activated carbon demonstrated the highest initial discharge specific capacity and stable reversible capacity after 50 cycles as 1462 and 477 mAh g−1 (1st and 50th cycle), and coulombic efficiency higher than 90 %. © 2023

This article presents the results of research intended to obtain a complex alumina-iron reagent based on natural diatomite and industrial products of alumina production for wastewater purification from hydrogen sulfide. The material composition of the obtained samples using X-ray diffraction analysis was determined. The results of interaction research in the NaFeO2 – H2S – H2O system at 25°С are given. The results of research on wastewater purification from hydrogen sulfide in Almaty city with the use of ferric sulfate, ferric chloride, sodium ferrite and a complex reagent containing iron at the content of 5.1 mg/l H2S in the initial sample of wastewater were presented © 2022. Journal of Ecological Engineering.All Rights Reserved.

The existing experience of noise and vibration specialists has shown that the problem of noise reduction is very relevant, especially for the mining industry. Traditional methods of dealing with industrial noise are not effective enough. In solving this issue, it is advisable to reduce the noise level at the source of its occurrence through the use of metal alloys with enhanced dissipative properties. The article presents the results of experimental studies of developing steels with increased damping properties for manufacturing perforator parts: bit bodies and drill rods. In this article, the sound pressure level of alloys dependence on the type of heat treatment has been studied, and the optimal content of alloying elements has been established to ensure the development of the ferrite-pearlite structure. This structure is characterized by an increased dislocation density and is the reason for reducing the noise of the drill rod and the body of the perforator bit by 10–12 dB A. In addition, the article establishes the pattern of noise intensity at different frequency intervals for standard and developed alloys. © 2023 The Authors

Currently, there is an urgent need for non-invasive monitoring of farm animals’ health status, enabling swift responses to adverse situations such as morbidity, feeding disorders, and aggression. Globally, technologies for video monitoring of animals are being developed, which include image processing using intelligent methods, especially artificial neural networks. This paper presents the results of developing and investigating methods and models for detecting (individually identifying) farm animals, with a focus on pigs as a case study. These animals are located in dense, dynamic groups within agricultural complexes where traditional identification methods are less effective. To overcome this challenge, advanced neural network architectures, specifically Faster R-CNN and YOLOv5, were selected, finely tuned, and trained. The application of the YOLOv5 network achieved a detection accuracy with a mean Average Precision (mAP) of 94.05%, surpassing the accuracy demonstrated in comparable studies. These results provide a foundation for a hardware-software complex designed for non-invasive, automated monitoring of animal conditions, integrating intelligent data analysis. This system offers crucial support for science-based decision-making in the fields of animal husbandry and food security management. © (2024) NSP Natural Sciences Publishing Cor.

The development of polyurethane materials and process optimization are currently the subjects of extensive study. Polyurethane is characterized by high physicochemical and operational properties. Polyurethanes have high wear resistance, and oil and gasoline resistance. They have excellent thermophysical and elastic properties. This allows the use of polyurethanes in many industries where materials with high-performance properties are required. Polyurethanes are widely used in many industrial applications, protective coating manufacturing, and anti-corrosion agent applications. A significant number of studies have been conducted to improve the physical, mechanical, and operational properties of polyurethane polymers, in particular the anti-corrosion properties of modified polyurethane coatings. The properties of polyurethane polymers for various applications can be improved by changing monomers and their ratios and the process of preparations. Preparation of polyurethane polymers based on polyols and isocyanate monomers using a polyaddition process in the presence of a catalyst as well as solvents including toluene, xylene, and acetone. There are different factors affecting the physical and mechanical properties of polyurethane polymers were investigated by different techniques. The factors were types of isocyanates, polyols, OCN/OH ratios, solvents, catalysts, and temperatures. Generally, the polyols are responsible for the flexibility of the polyurethane polymers and isocyanates are responsible for the rigidity of the polyurethane polymer and crosslinking between the backbone of the polymer. Because of the flexibility of its chemistry, they may modify the coating's characteristics based on the intended use. The effects of different polyols and polyisocyanates' chemistry are assessed. The hydrophobicity, thermal stability, and mechanical and anti-corrosion properties of polyurethane polymers were investigated. As a result, the properties of polyurethane polymers such as hydrophobicity, thermal stability, and mechanical and anti-corrosion properties were all enhanced when all the above factors. An outline of the most modern, financially successful methods for creating protective polyurethane coatings and using them as anti-corrosion agents is given in this review article. © 2024, Institute of Metallurgy and Ore Beneficiation JSC. All rights reserved.

In low-permeability sandstone reservoirs (LPSR), impermeable interlayers significantly challenge carbon capture, utilization, and storage (CCUS) and enhance oil recovery (CO2-EOR) processes by creating complex, discontinuous flow units. This study aims to address these challenges through a comprehensive multi-faceted approach integrating geological and microscopic analyses, including core analysis, reservoir petrography, field emission-scanning electron microscopy (FE-SEM), energy dispersive spectroscopy (EDS), and well-logging response analysis, and utilizing three-dimensional (3D) geological modeling. The current comprehensive investigation systematically characterizes interlayer types, petrophysical properties, thickness, connectivity, and their spatial distribution in the reservoir unit. Numerical simulations were conducted to assess the sealing efficiency and the impact of various interlayer materials on CO2 flooding over a 10-year period. Results indicate the presence of petrophysical and argillaceous interlayers, with optimal sealing occurring in petrophysical barriers ≥ 4 m and argillaceous barriers ≥ 1.5 m thick. CO2 leakage occurs through preferential pathways that emerge in a side-to-middle and bottom-to-top direction in interbeds, with multidirectional pathways showing greater leakage at the bottom compared to the upper side within barriers. Increased interlayer thickness constraints CO2 breakthrough but reduces vertical flooding area and production ratio compared to homogeneous reservoirs. Augmented interbed thickness and area mitigate CO2 breakthrough time while constraining gravity override and dispersion effects, enhancing horizontal oil displacement. These novel findings provide crucial insights for optimizing CCUS-EOR strategies in LPSR, offering a robust theoretical foundation for future applications and serving as a key reference for CO2 utilization in challenging geological settings of LPSR worldwide. © 2025 by the authors.

The degradation resistance growth of anode ceramic materials with the possibility of maintaining the stability of electrochemical and thermal conductivity properties under conditions of long-term high-temperature operation is one of the key directions in the development of solid oxide fuel cells. This article examines the possibility of modification of NiAl2O4 ceramics by adding aluminum nitride to them during the synthesis process, which leads to the formation of inclusions in the form of oxy-nitride grains. The interest in this class of ceramics is due to their structural features, which allows to consider them as one of the promising types of ceramics in the field of anode materials for solid oxide fuel cells. During assessment of the influence of impurity inclusions in the form of the oxy-nitride Al7O3N5 phase in the composition of NiAl2O4 ceramics on the deformation-induced swelling of the crystal structure of the damaged layer under high-dose irradiation, it was established that an elevation in the impurity phase concentration from 2.5 to 7.0 wt % results in swelling resistance growth by more than 4 times compared to NiAl2O4 ceramics without impurity inclusions. It was also determined that the presence of impurity inclusions in the composition of NiAl2O4 ceramics leads to a decrease in the coefficient of thermal volumetric expansion, a reduction of which indicates increased stability of the crystalline structure of ceramics to external temperature influences.

In the face of escalating cyberbullying and its associated online activities, devising effective mechanisms for its detection remains a critical challenge. This study proposes an innovative approach, integrating Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN), for the detection of cyberbullying in online textual content. The method uses LSTM to understand the temporal aspects and sequential dependencies of text, while CNN is employed to automatically and adaptively learn spatial hierarchies of features. We introduce a hybrid LSTM-CNN model which has been designed to optimize the detection of potential cyberbullying signals within large quantities of online text, through the application of advanced natural language processing (NLP) techniques. The paper reports the results from rigorous testing of this model across an extensive dataset drawn from multiple online platforms, indicative of the current digital landscape. Comparisons were made with prevailing methods for cyberbullying detection, demonstrating a substantial improvement in accuracy, precision, recall and F1-score. This research constitutes a significant step forward in developing robust tools for detecting online cyberbullying, thereby enabling proactive interventions and informed policy development. The effectiveness of the LSTM-CNN hybrid model underscores the transformative potential of leveraging artificial intelligence for social safety and cohesion in an increasingly digitized society. The potential applications and limitations of this model, alongside avenues for future research, are discussed. © (2023), (Science and Information Organization). All Rights Reserved.

Artificial intelligence (AI), machine learning (ML), and 6G networks may transform the Internet of Things. 6G networks provide minimal device latency and accommodate substantial data throughput. It also addresses network support issues inside the extensive IoT ecosystem. AI and ML enable 6G-enabled IoT platforms to allocate resources, comprehend context, and execute intelligent decisions for predictive maintenance and security. Nevertheless, energy efficiency, data confidentiality, and device interoperability pose challenges as systems become increasingly intricate and interconnected. This article advocates the systematic application of AI and ML to enhance the performance of 6G IoT networks. We illustrate how AI-driven network management may enhance connectivity, operational costs, and real-time decision-making in smart cities, healthcare, and industrial automation. This study investigates the role of federated learning and edge computing in addressing energy and privacy challenges within extensively distributed IoT systems. This paper presents a foundational reference model to inspire research that addresses gaps and integrates the global landscape, focusing on the dependable, efficient, and intelligent utilization of 6G-enabled IoT systems

Stressful events in students' and teachers' personal, academic, and professional lives are widespread. The paper discusses many effective methods and techniques for correcting and preventing stress that are simple to learn and practice. They give good results in working with children, adolescents, and youths in training and education. Some practices are more complex and require more attention and effort to understand and master them, but they also have broader capabilities in various situations. Therefore, training sessions aimed at training in the prevention and correction of (di)stress conditions are very relevant and necessary. The results showed that there is a conscious and urgent need for students to reduce anxiety and stress, including ways to deal with learning stresses. Stress management can and should be structured and systematically organized, including in the instrumental sense: students need to be taught how to manage stress and themselves, increase their resistance to stress (resilience), and be trained to use different coping techniques with anxiety, as appropriate. In an empirical study, representatives of other groups of students from three Kazakh universities answered questions from three author's stress questionnaires. According to the respondents, the study results showed that students need knowledge about stress. The study showed the urgent need for special educational and training seminars, lectures, and even courses on (di)stress and physical, mental, and moral injuries and coping with them. Such classes are needed to help schoolchildren and students cope with stress and avoid problems with moral, mental, and physical health to prevent other negative consequences of school and related strains. © 2023 by the author.