The study is devoted to the problem of processing the organic waste that is generated as a result of paper, textiles and other industries production as well as food waste. The growth of economic activity in Kazakhstan has resulted in a significant challenge with regard to industrial waste management. The accumulation of waste on the territory of the country has reached 31.72 billion tonnes, comprising approximately 2.5 billion tonnes of hazardous waste, 50 million tonnes of phosphorus-containing waste, over 2.5 million tonnes of lead-zinc waste and more than 120 million tonnes of solid domestic waste. The study object was the Shymkent-Kokys polygons. According to the research carried out, it was determined that the titer of microorganisms of the studied groups is 1–10 CFU/g in the soils selected around the garbage in the area of the Shymkent landfill. The titer of microorganisms in the soil horizons was high at a depth of 20–30 cm and the titer were 10⁹ cells/mL. The structure of the soil microbiome obtained around the Shymkent Waste Landfill consists of actinomycetes, micromycetes, heterotrophic bacteria, nitrifying, nitrogen-fixing bacteria, enterobacteria, as well as algae and protozoa. It was found that strains KPA1, KPA2 Pseudomonas sp. strains KPA3, KPA4, KPA5 Bacillus sp. isolated from the soils of the Shymkent-Kokys landfill are able to recycle domestic waste with a high content of cellulose and organic substances up to 95%–97%. The findings can be used to develop more effective organic cellulosic waste management strategies and improve the environmental sustainability of various industries.

Hospital waste containing antibiotics is toxic to the ecosystem. Ciprofloxacin is one of the essential, widely used antibiotics and is often detected in water bodies and soil. It is vital to treat these medical wastes, which urge new research towards waste management practices in hospital environments themselves. Ultimately minimizes its impact in the ecosystem and prevents the spread of antibiotic resistance. The present study highlights the decomposition of ciprofloxacin using nano-catalytic ZnO materials by reactive oxygen species (ROS) process. The most effective process to treat the residual antibiotics by the photocatalytic degradation mechanism is explored in this paper. The traditional co-precipitation method was used to prepare zinc oxide nanomaterials. The characterization methods, X-Ray diffraction analysis (XRD), Fourier Transform infrared spectroscopy (FTIR), Ulraviolet-Visible spectroscopy (UV-Vis), Scanning Electron microscopy (SEM) and X-Ray photoelectron spectroscopy (XPS) have done to improve the photocatalytic activity of ZnO materials. The mitigation of ciprofloxacin catalyzed by ZnO nano-photocatalyst was described by pseudo-first-order kinetics and chemical oxygen demand (COD) analysis. In addition, ZnO materials help to prevent bacterial species, S. aureus and E. coli, growth in the environment. This work provides some new insights towards ciprofloxacin degradation in efficient ways.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks' performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.

Energy systems face serious difficulties due to economic policy uncertainty, which affects consumption trends and makes the shift to sustainability more difficult. While adjusting for economic growth and carbon emissions, this study examines the dynamic relationship between economic policy uncertainty and energy consumption (including renewable and nonrenewable) in China from 1985Q1 to 2023Q4. The research reveals the frequency-specific and time-varying relationships between these variables by employing sophisticated techniques such as Wavelet Cross-Quantile Correlation (WCQC) and Partial WCQC (PWCQC). Economic policy uncertainty and energy consumption do not significantly correlate in the short term; however, over the long term, economic policy uncertainty positively correlates with renewable energy consumption at medium-to-upper quantiles, indicating that it may play a role in encouraging investments in sustainable energy. On the other hand, EPU has a negative correlation with nonrenewable energy usage at lower quantiles, indicating a slow move away from fossil fuels. These results are confirmed by robustness testing with Spearman-based WCQC techniques. The study ends with policy recommendations to maximize economic policy uncertainty’s long-term impacts on renewable energy, reduce dependency on fossil fuels, and attain environmental and energy sustainability in China.

The study examines the acceptance and sustainability of vegetarian, vegan, and flexitarian diets, focusing on the health and environmental benefits of reducing animal-derived proteins. Our objective was to investigate the level of acceptance of these dietary trends across different age groups and health statuses and understand how sustainability awareness and health consciousness impact dietary decisions. We used a mixed-method approach to achieve this, conducting eight in-depth interviews and a survey with 329 participants from various demographic backgrounds. Our qualitative analysis revealed that individual and family health consciousness, along with sustainability considerations, play a significant role in dietary choices, particularly among younger generations who are more open to sustainable eating. Quantitative results show that access to information and educational resources strongly influences dietary decisions, further supporting the spread of environmentally conscious eating habits. The practical significance of our research lies in highlighting the importance of educational campaigns and public health policies that can foster broader societal acceptance of sustainable diets. Educational institutions and community organizations can help facilitate the transfer of knowledge necessary for adopting such diets. Our findings emphasize the role of targeted communication strategies in increasing awareness of the benefits of plant-based diets. Furthermore, these insights underline the potential of policy interventions to make sustainable food choices more accessible and appealing to a wider population. Future research could focus on exploring economic incentives and examining long-term health and environmental outcomes associated with these diets.

Considering the role of tourism in promoting sustainable practices in destinations, this study aims to map the scientific literature on footprint calculators in the last three years (2020–2023) with a focus on the tourism context. The method adopted is a scoping review with a qualitative and exploratory approach, using the Scopus database. The originality of this research lies in the study of publications related to footprint calculators with a focus on the tourism sector. Based on the analysis carried out, the main results show that the study of footprint calculators applied to the tourism sector has had little prominence in the indexed research in the Scopus database during the specific period considered for this study. Consequently, the conclusion of the study highlights the marginality of the tourism sector in the discussion of footprint calculators in the last 3 years of scientific publications.