Assessment of water resources carrying capacity (WRCC) is of great significance for understanding the status of regional water resources, promoting the coordinated development of water resources with environmental, social and economic development, and promoting sustainable development. This study focuses on the Longdong Loess Plateau region and utilized panel data spanning from 2010 to 2020, established a three-dimensional evaluation index system encompassing water resources, economic, and ecological dimensions, uses the entropy-weighted TOPSIS model coupled with global spatial autocorrelation analysis (Global Moran’s I) and the hot spot analysis (Getis-Ord Gi* index) method to comprehensively evaluate the spatial distribution of the WRCC in the study region. It can provide scientific basis and theoretical support for decision-making on sustainable development strategies in the Longdong Loess Plateau region and other regions of the world.From 2010 to 2020, the overall WRCC of the Longdong Loess Plateau area show some fluctuations but maintained overall growth. The WRCC in each county and district predominantly fell within level III (normal) and level IV (good). The spatial distribution of the WRCC in each county and district is featured by clustering pattern, with neighboring counties displaying similar values, resulting in a spatial distribution pattern characterized by high carrying capacity in the south and low carrying capacity in the north. Based on these findings, our study puts forth several recommendations for enhancing the WRCC in the Longdong Loess Plateau area.

One crucial metric for estimating a reservoirs and dam’s lifespan is sedimentation. It is dependent upon sediment output, which in turn is dependent upon soil erosion. The study area, the Aguat Wuha Dam, was located in Simada woreda, of northwestern parts of Ethiopia. And the study's goal was to use Arc GIS and RUSLE adjusted to Ethiopian conditions to assess potential soil erosion and sediment output from the watershed and identify hotspot locations for appropriate planning for erosion and sedimentation problem management techniques to make the outputs of the dam project more productive and effective for the proposed and suggested purpose of the dam. To predict the geographical patterns of soil erosion in the watershed, the Geographic Information System (GIS) was combined with the revised universal soil loss equation (RUSLE). A soil erosion map was produced using ArcGIS by utilizing all of the model's parameters, including Erosivity, erodibility, steepness, land use, land cover, and supportive practice factors. The watershed's yearly soil loss varies from 0 to 413.86 tons/ha. In order to determine the erosion hotspot area, the average annual soil loss value was discovered to be 9.24 tons/ha/year and was categorized into six erosion severity classes: low, moderate, high, very high, severe, and very severe. These findings indicated that 162.57 ha and 699.17 ha of the watershed were considered to be extremely and severely vulnerable to soil erosion, respectively. It was discovered that the anticipated sediment yield supplied to the outlet varied from 0 to 104.94 tons/ha/year. By standing from the implications of the assessments of the geological, geotechnical, topographical, and socioenvironmental considerations Watershed management is the most effective way to reduce the amount of sediment produced and the amount that enters the reservoir among the several reservoir sedimentation control options that are available.

The purpose of the article is to present the results of analysis of newly industrialized countries in the context of sustainable development. The study took place within the framework of the Kaldor’s structural-economic model of the gross domestic product and the energy flow model, using the socio-economic systems power changes analyzing method. Within the context of the approach, an invariant coordinate system in energy units is considered, the necessary conditions for sustainable development are formulated, and the main parameters for assessing the potential for growth and development are determined. The article focuses on key issues regarding new concepts of sustainable development and methodology for assessing sustainable development using the concept of socioeconomics useful power for the countries of the newly industrialized economy a group of emerging countries that have made in short time period a qualitative transition in socio-economic development. Based on a new definition of sustainable development in energy units, development trends are formulated for the selected countries during 20 years for the period 2000–2019. Results of the study can be used to planning for the transition to sustainable development. The data of the Central Statistical Office of European Union, the World Bank and the United Nations Organization were used for calculations. Initial interpretation of the calculated data has been done for the largest newly industrialized countries Brazil, India and China in terms of the gross domestic product in the period 1990–2019. For comparison, data on USA are presented as countries with advanced economy.

Photocatalysis, an innovative technology, holds promise for addressing industrial pollution issues across aqueous solutions, surfaces, and gaseous effluents. The efficiency of photodegradation is notably influenced by light intensity and duration, underscoring the importance of optimizing these parameters. Furthermore, temperature and pH have a significant impact on pollutant speciation, surface chemistry, and reaction kinetics; therefore, process optimization must consider these factors. Photocatalytic degradation is an effective method for treating water in environmental remediation, providing a flexible and eco-friendly way to eliminate organic contaminants from wastewater. Selectivity in photocatalytic degradation is achieved by a multidisciplinary approach that includes reaction optimization, catalyst design, and profound awareness of chemical processes. To create efficient and environmentally responsible methods for pollution removal and environmental remediation, researchers are working to improve these components.

In the present work, a series of butyl methacrylate/1-hexene copolymers were synthesized, and their efficiency as viscosity index improvers, pour point depressants, and shear stabilizers of lube oil was investigated. The effect of 1-hexene molar ratio, type, and concentration of Lewis acids on the incorporation of 1-hexene into the copolymer backbone was investigated. The successful synthesis of the copolymers was confirmed through FTIR and 1H NMR spectroscopy. Results obtained from quantitative 1H NMR and GPC revealed that an increase in the molar ratio of 1-hexene to butyl methacrylate, along with concentration of Lewis acids led to an increase in 1-hexene incorporation and a reduction in Mn and Ð. Similar trends were observed when the Lewis acid changed from AlCl₃ to organometallic acids. The maximum 1-hexene incorporation (26.4%) was achieved for sample BHY3, with a [1-hexene/BMA] ratio of 4 mol% and a [Yb(OTf)₃/BMA] ratio of 2.5 mol%. Evaluation of the synthesized copolymers as lube oil additives demonstrated that the viscosity index was more significantly influenced by samples with higher molecular weight. Sample BHA13 represents maximum VI of 137. The copolymer containing Yb(OTf)₃ as a catalyst exhibited superior efficiency as a pour point depressant. Furthermore, sample BHY3 showed the lowest shear stability index (6.4).

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.