Water scarcity and contamination are critical global concerns, with organic pollutants such as dyes, phenolic compounds, pharmaceuticals, and pesticides increasingly entering aquatic systems and posing serious risks to ecosystems and human health. Among various treatment technologies, photocatalysis has emerged as a sustainable and effective strategy for wastewater purification, particularly for degrading persistent organic contaminants. However, conventional metal-semiconductor photocatalysts suffer from inherent drawbacks, including limited visible-light absorption, sluggish surface kinetics, and rapid electron–hole recombination, which restrict their large-scale applicability. These challenges can be addressed by incorporating graphene and its derivatives graphene oxide (GO) and reduced graphene oxide (rGO) which provide high surface area, excellent electron mobility, and tunable chemical functionality, making them ideal co-catalyst supports. Unlike numerous reviews that broadly summarize graphene-based photocatalysts, the present work adopts a pollutant-specific comparative framework. It systematically evaluates the performance of graphene, GO, and rGO-based composites in degrading four major categories of contaminants: dyes, phenolic compounds, pharmaceuticals, and pesticides. Special emphasis is placed on understanding the role of operational parameters such as pH, oxidizing agents, light wavelength, photocatalyst dosage, and pollutant concentration in influencing degradation outcomes. Mechanistic insights, recyclability, and efficiency trends are critically examined through case studies, highlighting both synergies and limitations across pollutant classes. By adopting this targeted approach, the review not only underscores key advancements but also identifies existing knowledge gaps, offering valuable perspectives for designing next-generation graphene-based photocatalysts tailored to specific water pollutants. © 2025 The Authors

The following article studies research on cognitive technologies and their application in the transportation of mining waste. Special attention is paid to the development and implementation of intelligent systems which are able to automate the processes of monitoring, analysis and waste recycling. There were discussed the examples of using machine learning and artificial intelligence to improve the efficiency of waste management, minimize their negative impact on the environment and improve the economic indexes of companies. Cognitive technologies allow transport companies to expand their functions in summarizing and processing huge volumes of structured and unstructured data, use machine learning tools for data analysis, execute queries and get the most accurate answers, at the same time ensuring the safety of cargo. In order to increase the effectiveness and speed of transportation, customer service at all stages and at the same time reduce the effect of the “human factor”, it is necessary to develop an offer that will be most optimal for everyone. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

The development of an active ankle prosthesis aims to overcome the limitations of traditional passive prostheses by introducing dynamic and adaptive functionalities, enhancing mobility, stability, and comfort for individuals with lower limb amputations. This paper presents the conceptual framework of a CAD-modelled active ankle prosthesis, specifically designed to improve walking efficiency. The proposed prosthesis integrates biomechanical principles to replicate natural gait while employing advanced control systems for real-time adaptability and rapid response to movement variations. Key components of the design include an electric-driven actuation mechanism for dynamic foot bending system, a robust control system, and an ergonomic structure that ensures user comfort and safety. The prosthesis is constructed using Nylon 6 (PA6), which has a Modulus of Elasticity (E) of 8.3 GPa (8.3 × 10⁹ N/m2) and a Poisson’s Ratio (ν) of 0.28. These material properties are crucial for maintaining structural integrity and mechanical performance under different loading conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

The test of the hydrogen generator on the internal combustion engine of the car is considered in the work. In the machine, HHO and fuel are jointly burned to reduce fuel consumption and exhaust emissions. Numerous studies prove the undeniable advantage of using a car using a hydrogen generator compared to traditional fuels. The use of a lean combustible mixture in internal combustion engines reduces the volume of exhaust gases and increases thermal efficiency. Experimental results show that by adding KOH with a concentration of (35-50 g/l) to the cell, fuel consumption can be reduced (8.4-43.6%), and exhaust pollutant emissions are reduced. Thus, adding hydrogen to an internal combustion engine can reduce fuel consumption and vehicle exhaust emissions.

The growing environmental and energy crises have prompted researchers to seek new solutions, including large-scale photocatalytic environmental remediation and the production of solar hydrogen using photocatalytic materials. To achieve this goal, scientists have developed numerous photocatalysts with high efficiency and stability. However, the large-scale application of photocatalytic systems under real-world conditions is still limited. These limitations arise at every step, including the large-scale synthesis and deposition of photocatalyst particles on a solid support, and the development of an optimal design with high mass transfer and efficient photon absorption. The purpose of this article is to provide a detailed description of the primary challenges and potential solutions encountered in scaling up photocatalytic systems for use in large-scale water and air purification and solar hydrogen production. Additionally, based on a review of current pilot developments, we draw conclusions and make comparisons regarding the main operating parameters that affect performance, as well as propose strategies for future research. © 2023 Elsevier B.V.

A mathematical model of the epidemic development under conditions of limited work of immunization acquired both after recovery and through vaccination is considered. The model is characterized by a nonlinear system of differential equations with some equilibrium position. Based on numerical analysis, it is shown that the state of the system stabilizes after some time and tends to a state that is the equilibrium position not of the original, but of some simplified system. The results obtained have a natural interpretation.

Spatial disparities in rangeland conditions across Kazakhstan complicate field-based assessments of livestock-carrying capacity (LCC), a critical metric for the country’s food security and economic planning. This study developed a geospatial livestock-carrying capacity (GLCC) modeling framework to quantify LCC spatio-temporal dynamics at the Oblast level, by integrating satellite-derived data on vegetation, water resources, and terrain with in situ measurements. By providing ground-truth observations and contextual detail, field-based measurements complement remote sensing data and help to validate estimates and improve the reliability of the GLCC model. The modeling framework was successfully applied and validated in a case study in the Akmola Oblast, Kazakhstan, to specifically map the spatial and temporal distributions of LCC, using publicly available MODIS NPP data and in situ data from 51 field sites. The modeling results showed distinct spatial patterns of LCC across the Oblast, reflecting variability in rangeland productivity with higher values concentrated in southern and southeastern regions (up to 0.5 animals/ha). The results also depicted significant interannual LCC fluctuations (ranging from 0.099 to 0.17 animals/ha) possibly due to rainfall variability, and thus an indicator of climate-related risks for livestock management. Although there is still room for further improvement, particularly in model parameterization to account for grazing pressures, forage quality, and livestock species, the GLCC modeling framework represents a simple modeling tool to map livestock-carrying capacity, a more meaningful indicator to rangeland managers. Further, this work underscores the value of integrating remote sensing with field-based observations to support data-driven rangeland management planning and resilient investment strategies. © 2025 by the authors.

Recently, hybrid (organic-inorganic) metal halide perovskites have gained significant attention due to their excellent performance in optoelectronics and photovoltaics (PV). Single-junction PV cells made from these materials have achieved record efficiencies of over 25%, with the potential for further improvement in the future. The crystal structure of organohalide perovskite semiconductors plays a crucial role in the success of perovskites. In this study, we used classical all-atom molecular dynamics simulations to investigate the dynamics of ionic precursors as they form organic halide perovskite units in the presence of water as a solvent. During the analysis of radial distribution functions, interaction energies, hydrogen bonding, and diffusion coefficients, it was confirmed that organic precursors aggregate in the absence of water and disperse in the presence of water. The interaction energies also showed that the organic precursors of the perovskite have weaker interactions with Pb than the other components of the perovskite. The hydrogen bonding analysis revealed that the number of hydrogen bonds between the organic precursors and Cl decreases in the presence of water, but hydrogen bonds form between the organic precursors/water and Cl/water. Additionally, the diffusion coefficients of the organic precursors were found to be in the following increasing order: 2,2-(ethylenedioxy) bis ethylammonium (EDBE2+) < guanidium (GA+) < phenethylammonium (PEA+) < iso-butylammonium (Iso-BA+).

The study critically analyzes the innovation challenges faced by mining enterprises in Kazakhstan, focusing on key issues and potential opportunities related to the adoption of Industry 4.0 technologies. The research highlights that the strategic goal of Kazakhstan's mining sector is its integration into the global economy through digitalization, which aims to address the sector’s limited production of high-value goods. Despite the potential of digital transformation, the current level of digitalization across enterprises remains insufficient, requiring more structured implementation of engineering and economic processes. A statistical analysis using political, economic, social, technological, environmental and legal (PESTEL), regression, and factor analysis, alongside a questionnaire survey, revealed low capital productivity as a major issue. The findings suggest that the complexity of mining operations necessitates improved innovation strategies tailored to specific resource extraction conditions. Furthermore, the digitalization focus is gradually shifting from production to areas like geology, ecology, and industrial safety. Digitalization has resulted in a 15% increase in productivity and efficiency. The study offers a framework for evaluating mining enterprises against international standards and serves as a valuable reference for both domestic and international investors interested in the evolving information technology (IT) landscape of Kazakhstan's mining sector.

This study integrates classical all-atom molecular dynamics (MD) simulations and density functional theory (DFT) calculations to explore bisphosphate interactions in carbon fiber reinforced polymers (CFRP) composed of graphene oxide and epoxy resin for wind turbine applications. This research addresses the growing need for durable and reliable materials in renewable energy technologies. MD simulations reveal favorable interaction energies between bisphosphate and the graphene oxide/epoxy resin composite, suggesting improved molecular compatibility and structural stability. Radial distribution function (RDF) analysis and MD snapshots provide a detailed view of bisphosphate's spatial arrangements and interactions within the composite matrix. Complementary DFT calculations highlight optimized energy configurations and electronic structures, emphasizing bisphosphate's electron-donating and accepting capabilities. These findings underscore bisphosphate's potential to enhance the performance and resilience of graphene oxide and epoxy resin composites under demanding environmental conditions. By offering atomistic insights into material behavior, this study supports the development of advanced composite materials for wind turbine technologies, contributing to the sustainability and efficiency of renewable energy infrastructure. Further experimental validation is recommended to translate these computational findings into practical applications. © The Author(s) 2025.