The article addresses the issue of educational development policy in Ukraine: the main trends and ways, means, technologies of their implementation. It has been observed that educational policy is developing and changing under the influence of such factors as Russia’s military actions against our country, European integration and globalisation. It has been taken into account that globalisation trends in the world integration, according to which globalisation processes should be reflected not only in the foreign economic, political or technological spheres, but also, as a consequence, in the development of technologies for training future teachers. Integration of digital technologies in the educational process is one of the key tendencies in the modern educational policy in Ukraine. The characteristics of the most used technologies of augmented reality in the modern school of Ukraine have been outlined. The algorithm for displaying generalized information about a particular application was proposed, namely: payment, accessibility, language, system requirements; learning opportunities; practical value; website; video about the application. The model of the formation of future teachers’ skills to use augmented reality technologies in the process of natural sciences studying has been proposed. We consider it as a component of a holistic system of future teachers’ professional training. The conceptual basis for the development of the model is a multi-subject educational paradigm, which is considered to be open, self-developing and self-organizing, causing a fundamental change in the behavior and relationships of the educational process participants. The proposed model is implemented in the authors’ methodological system, which ensures the interconnected activities of all participants in the educational process. Its systemic factor is the goal of improving the quality of the future natural sciences teachers’ professional training by developing their skills in using AR technology. The end result is an increase in the level of future natural sciences teachers’ readiness to use AR technology in their professional activities.

In response to the increasing global emphasis on sustainability and the specific challenges faced by small and medium-sized enterprises (SMEs) in China, this study explores the integration of green reverse logistics within these enterprises to enhance their sustainability and competitiveness. The aim of this study is to understand the relationship between reverse logistics, green logistics, and sustainable development. Data analysis was conducted utilizing a combination of descriptive statistics and correlation analysis. A survey of 311 participants examined SMEs’ performance in reverse logistics practices and their initiatives in green logistics and sustainable development. The empirical findings reveal significant progress in reverse logistics practices among SMEs, reducing environmental impact and improving resource efficiency. Moreover, a notable positive correlation was identified between reverse logistics promotion and advancements in green logistics and sustainable development. SMEs’ investment in reverse logistics is closely linked to their efforts in environmentally conscious and sustainable supply chain management. These insights benefit SMEs and supply chain practitioners and offer a valuable reference for future research and practical applications in this field.

Background: Bitcoin mining, an energy-intensive process, requires significant amounts of electricity, which results in a particularly high carbon footprint from mining operations. In the Republic of Kazakhstan, where a substantial portion of electricity is generated from coal-fired power plants, the carbon footprint of mining operations is particularly high. This article examines the scale of energy consumption by mining farms, assesses their share in the country’s total electricity consumption, and analyzes the carbon footprint associated with bitcoin mining. A comparative analysis with other sectors of the economy, including transportation and industry is provided, along with possible measures to reduce the environmental impact of mining operations. Materials and methods: To assess the impact of bitcoin mining on the carbon footprint in Kazakhstan, electricity consumption from 2016 to 2023, provided by the Bureau of National Statistics of the Republic of Kazakhstan, was used. Data on electricity production from various types of power plants was also analyzed. The Life Cycle Assessment (LCA) methodology was used to analyze the environmental performance of energy systems. CO₂ emissions were estimated based on emission factors for various energy sources. Results: The total electricity consumption in Kazakhstan increased from 74,502 GWh in 2016 to 115,067.6 GWh in 2023. The industrial sector’s electricity consumption remained relatively stable over this period. The consumption by mining farms amounted to 10,346 GWh in 2021. A comparative analysis of CO₂ emissions showed that bitcoin mining has a higher carbon footprint compared to electricity generation from renewable sources, as well as oil refining and car manufacturing. Conclusions: Bitcoin mining has a significant negative impact on the environment of the Republic of Kazakhstan due to high electricity consumption and resulting carbon dioxide emissions. Measures are needed to transition to sustainable energy sources and improve energy efficiency to reduce the environmental footprint of cryptocurrency mining activities.

This study explores the spatial distribution pattern of educational infrastructure development across districts and cities in North Sumatra, identifying significant disparities between urban and rural areas. The study aims to: (1) determine the distribution of educational development across districts and cities, (2) analyze global spatial autocorrelation, and (3) identify priority locations for educational development policies in North Sumatra Province. The methodology includes quantile analysis, Moran’s Global Index, and Local Indicators of Spatial Autocorrelation (LISA) using GeoDa software to address spatial autocorrelation. The results indicate that there are nine areas with a low School Participation Rate Index (SPRI), eleven areas with a low School Facilities and Infrastructure Index (SFII), and eleven areas with a low Regional Education Index (REI). Spatial autocorrelation analysis reveals that SFII shows positive spatial autocorrelation, while SPRI and REI exhibit negative spatial autocorrelation, indicating a high level of inequality between regions. Labuhan Batu Selatan and Labuhan Batu are identified as priorities for the provincial government in overseeing educational development policies.

Despite the proliferation of corporate social responsibility (CSR) studies, it is accruing academic interest since there still remains a lot to be further explored. The purpose of the study is to examine whether/how CSR perception affect employee/intern thriving at work and its mediator through perceived external prestige in the hospitality industry. Data from 501 hospitality industry employees and interns in China were collected using a quantitative survey consisting of 35 questions. Statistical findings showed that CSR perception and thriving at work were positively related. Additionally, perceived external prestige partially mediated the connection between CSR perception and thriving at work. Furthermore, the study found that hotel interns generally exhibited lower levels of CSR perception and thriving at work compared with frontline or managerial staff. The study underscores the importance of collaborative efforts between hotel practitioners and university educators to enhance CSR perception and promote thriving among hotel interns. By prioritizing the improvement of CSR perception and thriving at work, the hotel sector can potentially mitigate workforce shortages and reduce high turnover rates.

Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).