According to official data, modern Russia has the lowest unemployment rate. However, there is still a huge contingent of hidden unemployment, many times higher than the official level. This situation is paradoxically combined with an acute and continuously growing shortage of qualified production personnel. Using a lot of factual material, the author reveals the causes of this phenomenon. The main one is the depopulation of the indigenous population, which is being replaced by people of other ethnic groups with the lowest qualification level. At the same time, due to the destruction (“optimization”) of the education system, the intellectual and qualification level of the indigenous population is continuously decreasing. The other is the various types and waves of growing emigration of “brains” and “golden hands.” As a result, for more than thirty years, the contingent of old engineering and technical personnel has exhausted itself, while new ones have not been trained in the required volume and quality. A huge personnel “hole” has formed. The author proposes to close this “hole” on the basis of a radical reorientation of the entire Russian education system, starting with kindergarten, school, etc. It is also necessary to reformat the public consciousness accordingly, especially the mass consciousness of young people.

Mangrove forests are vital to coastal protection, biodiversity support, and climate regulation. In the Niger Delta, these ecosystems are increasingly threatened by oil spill incidents linked to intensive petroleum activities. This study investigates the extent of mangrove degradation between 1986 and 2022 in the lower Niger Delta, specifically the region between the San Bartolomeo and Imo Rivers, using remote sensing and machine learning. Landsat 5 TM (1986) and Landsat 8 OLI (2022) imagery were classified using the Support Vector Machine (SVM) algorithm. Classification accuracy was high, with overall accuracies of 98% (1986) and 99% (2022) and Kappa coefficients of 0.97 and 0.98. Healthy mangrove cover declined from 2804.37 km2 (58%) to 2509.18 km2 (52%), while degraded mangroves increased from 72.03 km2 (1%) to 327.35 km2 (7%), reflecting a 354.46% rise. Water bodies expanded by 101.17 km2 (5.61%), potentially due to dredging, erosion, and sea-level rise. Built-up areas declined from 131.85 km2 to 61.14 km2, possibly reflecting socio-environmental displacement. Statistical analyses, including Chi-square (χ2 = 1091.33, p < 0.001) and Kendall's Tau (τ = 1, p < 0.001), showed strong correlations between oil spills and mangrove degradation. From 2012 to 2022, over 21,914 barrels of oil were spilled, with only 38% recovered. Although paired t-tests and ANOVA results indicated no statistically significant changes at broad scales, localized ecological shifts remain severe. These findings highlight the urgent need for integrated environmental policies and restoration efforts to mitigate mangrove loss and enhance sustainability in the Niger Delta.

The electrospinning precursor solution was prepared by dissolving polyvinyl pyrrolidone as template, tetrabutyl titanate as titanium source, and acetic acid as inhibitor. The TiO₂ nanofilms were prepared by precursor solution electrospinning and subsequent calcination. Thermal gravimetric analysis (TG), scanning electron microscopy (SEM), X-ray powder diffraction (XRD), and transmission electron microscopy (TEM) were used to characterize and analyze the samples. The influence of technological parameters on spinning fiber morphology was also studied. The results indicate that the TiO₂ nanofibers morphology is good when the parameters are as follows: voltage 1.4×10⁴ V，spinning distance 0.2 m，translational velocity 2.5×10^-3 m·s^-1, flow rate 3×10^-4 m·s^-1, and needle diameter 3×10^-4 m. The diameter of the fibers is about 150 nm. With the 1×10^-4 mol·L^-1 methylene blue solution used as simulated degradation target, the degradation rate is 95.8% after 180 minutes.

Tropical peat swamp is an essential ecosystem experiencing increased degradation over the past few decades. Therefore, this study used the social-ecological system (SES) perspective to explain the complex relationship between humans and nature in the Sumatran Peatlands Biosphere Reserve. The peat swamp forest has experienced a significant decline, followed by a significant increase in oil palm and forest plantations in areas designated for peat protection. Human systems have evolved to become complex and hierarchical, constituting individuals, groups, organizations, and institutions. Studies on SES conducted in the tropical peatlands of Asia have yet to address the co-evolutionary processes occurring in this region, which could illustrate the dynamic relationship between humans and nature. This study highlights the co-evolutionary processes occurring in the tropical peatland biosphere reserve and provides insights into their sustainability trajectory. Moreover, the coevolution process shows that biosphere reserve is shifting toward an unsustainable path. This is indicated by ongoing degradation in three zones and a lack of a comprehensive framework for landscape-scale water management. Implementing landscape-scale water management is essential to sustain the capacity of peatlands social-ecological systems facing disturbances, and it is important to maintain biodiversity. In addition, exploring alternative development pathways can help alter these trajectories toward sustainability.

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.

Overwhelming studies unanimously agreed that preservation of the environment is a central climax in the discourse of green banking. There is a growing interest in exploring green banking practices for fostering financial inclusion, economic growth and sustainable development as part of Vision 2030 in Saudi Arabia. There are insufficient studies that examine this in the context of Saudi Arabia. This study aims at exploring the potential of green banking in order to attain sustainable banking and financial inclusion in achieving vision 2030in the country. Qualitative content analysis is used as a methodology of the study. Data were gathered through different sources such as: Web of Science (WOS), related journals, newspapers, published references, research papers, library sources and environmental organizations reports. It is indicated that green banking initiatives can be instrumental in fostering sustainable economic and environmental development in the Kingdom. The paper highlighted various activities of green banking such as: renewable and clean energy, financing green agriculture/food security, high-quality infrastructure among others. Nonetheless, some impediments to the green banking practices such as: risks facing green banks, poor quality of financial services among others are also mentioned in this paper. The paper proffers solutions to the challenges impeding green banking practices. In conclusion, the financial and banking industries in Saudi Arabia has been proving reform of the sector through greening economy. It is there suggested that the stakeholders and policymakers should provide efficient and effective technical, operational legal frameworks for enhancing green economy in achieving Vision 2030 in the country.