Personal and technological determinants of electric vehicle adoption in Oman: The moderating role of government innovation capability

The adoption of electric vehicles (EVs) is a crucial step toward sustainable transportation, yet its expansion in emerging markets like Oman faces several challenges. Limited charging infrastructure, technological readiness, and policy support impact the adoption rate. This study investigates the interplay between personal and technological determinants influencing EV adoption in Oman, emphasizing the moderating role of government innovation capability. A structured survey was conducted among 410 decision-makers from key regulatory bodies, and data were analysed using structural equation modelling. The research confirms that social norms, together with perceived usefulness and driver IT competency and system quality, substantially boost EV adoption, but insufficient charging facilities continue as the primary impediment. The capability for government innovation acts as a moderator that alters how social norms, system quality, and charging infrastructure influence the adoption of EVs. The study shows that EV adoption requires both governmental policy intervention and infrastructure development and consumer education programs for successful implementation. This research connects government innovation capability to established models, guiding both policymakers and industry leaders who want to develop a technologically progressive EV ecosystem. Research should expand beyond this study by using multiple stakeholder perspectives and combining longitudinal timeframes and various research methods to achieve better insights into electric vehicle adoption patterns

Agrotourism as an Instrument of Sustainable Development of the Rural Economy: A Case Study of Central and Eastern Europe

This study investigates the factors influencing the success of agrotourism as a sustainable rural development strategy in Central and Eastern Europe (CEE). Using a mixed-methods approach, data were collected from 200 respondents, including agrotourism operators, community members, and policymakers, and analysed with logistic regression. The findings highlight that tertiary education, community participation, farm size, sustainability practices, and tourist volume significantly impact success, while age shows a negative correlation. Specifically, 60% of tertiary-education operators leveraged innovative marketing and sustainable farming techniques, achieving higher success rates. Community participation emerged as the strongest predictor (odds ratio = 6.126), with farms engaging local communities, attracting an average of 820 tourists annually and generating notable revenue. Larger farms (mean size = 12.3 hectares) succeeded by offering diverse activities, while eco-friendly practices (mean sustainability index score = 3.8 out of 5) appealed to environmentally conscious tourists. Older operators (mean age = 45.2 years) faced challenges adopting digital tools, highlighting the need for tailored capacity-building programs. Additionally, the study explores the unique opportunities and challenges presented by mountainous terrain, which offers potential for eco-tourism due to its natural beauty and biodiversity but requires targeted infrastructure investments and conservation efforts. The study recommends educational initiatives, community-based tourism promotion, financial incentives for sustainability, and improved infrastructure to enhance accessibility

Solubility of phytochemicals and challenges in in vitro studies: a literature review

Poor solubility remains a critical barrier in the in vitro evaluation of phytochemicals, many of which are hydrophobic and difficult to dissolve in aqueous media. This review explores the physicochemical factors influencing phytochemical solubility, emphasizing the role of solvent properties such as polarity, proximity, and cytotoxicity. Commonly used solvents—including polar protic, polar aprotic, and non-polar solvents —are discussed concerning their solubilizing capacity and compatibility with biological systems. Solvent-induced changes in membrane dynamics and cytotoxic profiles are also examined, highlighting the need for cautious selection and optimization. Several advanced strategies to enhance solubility, such as co-solvent systems, pH modulation, nanocarrier encapsulation, surfactants, and deep eutectic solvents (DESs), are reviewed. A focused case study on curcumin illustrates how different solubilization methods can significantly improve in vitro performance. The review underscores the importance of standardized solvent reporting to ensure reproducibility and reliability in phytochemical research

Problems and prospects for the implementation of artificial intelligence in the educational process of Kazakhstani Universities

Integrating artificial intelligence into education offers great prospects that improve the learning process. Individualization of learning approaches and rationalization of resource use are very productive for learning. This study analyzes the key challenges and opportunities for implementing artificial intelligence into the educational system of Kazakhstani universities. The work focuses on analyzing the current situation and ways to improve this process. This article analyzes the problems and prospects for implementing artificial intelligence into education. The aim of the work was to identify the main challenges and opportunities for integrating artificial intelligence into the educational environment of Kazakhstan.  The article uses an analytical method. It is aimed at studying the current state of the infrastructure, the readiness of teachers, and the necessary conditions for the effective use of artificial intelligence technologies. The results of the work demonstrate the significant potential of artificial intelligence to improve the educational process. Personalization of learning and simplification of complex scientific concepts are the main advantages. The work highlights key obstacles to implementing artificial intelligence in education in Kazakhstan. Among them is the lack of technical resources and specialized training of teachers. For the full integration of artificial intelligence into the education system of Kazakhstan, there is a need to modernize the infrastructure, improve teacher training, and develop national initiatives

Blockchain-enabled carbon tracking in the oil Industry: A simulation-based study supporting ESG integration

The oil industry is increasingly being compelled to reconcile the complexity of its carbon-intensive business with Environmental, Social, and Governance (ESG) aims. ESG compliance is being handicapped by existing carbon reporting frameworks that are commonly fragmented, audit-driven, and subject to data falsification. This study explores how the use of blockchain technology can improve data integrity, traceability, and compliance costs and, as a result, transform oil supply chain carbon emissions monitoring. Through simulation-based evaluation, we are comparing systems supported by blockchain to conventional emission reporting systems through performance metrics such as cost of verification, audit lag, and traceability accuracy. To record carbon data at all locations, the simulation is combining smart contracts and decentralized ledger nodes to replicate a regional upstream midstream oil supply chain. In accordance with the study, blockchain integration enhances audit effectiveness by 91%, traceability by 36%, and lowers verification costs up to 70%. The study recognizes blockchain as a key digital infrastructure for sustainable business functioning and gives insightful recommendations to make ESG reporting easier for heavy industries

Using big data analytics to assess educational outcomes at Ukrainian universities

The proposed article aims to analyze the perception of teachers and students regarding the use of Big Data analytics to assess educational outcomes at Ukrainian universities. The primary method of information collection is interviews with survey elements. A purposive method was used to find and attract participants. In total, 15 teachers and 89 students participated. The results show that all participants in the educational process are ready to work with modern technologies. Among the advantages of big data analytics are enhanced tracking of student results, data-driven decision-making, and personalization of curricula. The study also identifies certain risks: problems with security, the use of incomplete or biased data, dependence on algorithms, financial barriers, dehumanization of education, and dependence on technology. The conclusions indicate that further implementation of these technologies requires the introduction of moderate solutions to analyze information

Cutting tools and applications

Cutting tools are one of the basic elements of modern industrial production, providing high efficiency and precision in machining different materials. This article examines the basic materials such as high-speed steels, stellites, hard metals and cermets, which are frequently used in cutting tool technologies, and their applications in machining processes. High speed steels (HSS) are preferred in a wide range of applications due to their superior wear resistance and impact resistance. These steels, enhanced with alloying elements such as carbon, vanadium and molybdenum, are widely used in machine tools operating at low speeds. Stellites are generally cobalt-based alloys that stand out with their high hardness and corrosion resistance. These materials are preferred in special applications by maintaining wear resistance even at high temperatures. Hard metals consist of components such as tungsten carbide (WC) and offer excellent performance at high cutting speeds. These materials are widely used in sectors requiring precision machining such as automotive and aerospace. Cermets are a combination of ceramic and metal phases and combine high hardness with chemical stability. It is especially preferred in applications where fine stock removal and surface quality are important. As a result, each cutting tool material offers specific advantages and limitations depending on the application requirements. The right material selection optimizes tool life and workpiece surface quality while increasing production efficiency

Technical-economic evaluation of a portable solar machine of potential use in pumping systems and water purification, through the use of photovoltaic solar technology for non-interconnected areas in Colombia

The purpose of this article is to present the dimensioning of a portable, autonomous solar machine, of potential utility in pumping systems and water purification, through the use of photovoltaic solar technology and inno-vation to an unsatisfied basic need, such as service drinking water, in areas of the country, where there are inconveniences for access to drinking water and electricity service. The solar machine is innovative, modular, portable, easy to transport and install, sustainable and environmentally friendly, with zero greenhouse gas emissions as it is powered by photovoltaic technology. The solar machine is innovative, modular, portable, easy to transport and install, sustainable and environmentally friendly, with zero greenhouse gas emissions as it is powered by photovoltaic technology and does not use batteries. This portable solar machine substantially reduces the cost of investment, operation and maintenance, by using photovoltaic solar panels and working with direct current, eliminating the use of the conventional electrical power network, inverters, for its opera-tion and can be dimensioned for water. surface water from rivers or reservoirs to the reservoir or pond, the same as for groundwater from wells, for the supply of drinking water, through the improvement, disinfection and sterilization of drinking water, to improve the quality of life of rural communities and secondly measure for use in the agricultural sector, for irrigation, domestic use, livestock (livestock pasture) and other services, for the rural development of the regions in Colombia

Quantitative assessment of the effects of climate change on water resources in the Huancané River basin, Peruvian Andes

The purpose of this article is to evaluate the effects of climate change on surface runoff, aquifer recharge, percolation and renewable water resources in the Huancané River basin, Puno – Peru, using the SWAT hydrological model and the standardized precipitation index (SPI). The RCP 4.5, RCP 8.5, SSP1-2.6 and SSP3-7.0 climate scenarios of CMIP5 and CMIP6 were applied for the projected period 2025–2100. The model was calibrated and validated with historical data from the period 1981–2016. The results showed that surface runoff will decrease significantly in the most extreme scenarios, reaching only 8.09 m³/s in SSP3-7.0, while in RCP 8.5 a maximum of 12.59 m³/s is projected. The recharge of the aquifer will be reduced from 559.22 Mm³ to 179.09 Mm³ and the volume of renewable water will decrease by 51.2%, from 750 Mm³ to 366 Mm³. In addition, the average annual temperature in the basin could reach 14°C by the end of the 21st century, increasing evapotranspiration and further reducing water availability. The SPI index projects an intensification of droughts during the period 2025–2050. These scenarios show a growing vulnerability of the water system, which represents a critical challenge for agriculture, supply and sustainability. The integration of the SWAT model with climate projections is a key tool for water planning and adaptation in vulnerable Andean regions

Using big data to increase the efficiency of business processes in the digital economy of Ukraine

This study explores the transformative role of big data tools in enhancing business efficiency within Ukraine\u27s digital economy. Using a cross-sectional design, data were collected from 200 managers and experts across diverse industries through a semi-structured questionnaire. The analysis encompassed descriptive statistics, reliability testing, exploratory factor analysis (EFA), regression analysis, and cluster analysis to examine the adoption of predictive analytics, business intelligence, and process automation. Results highlight process automation as the most significant efficiency driver, followed by predictive analytics and business intelligence, enabling streamlined workflows, faster decision-making, and reduced operational costs. Cluster analysis identified three distinct groups of organizations: high adopters achieving notable efficiency gains, moderate adopters facing substantial barriers, and low adopters with targeted benefits but limited efficiency gains. Barriers such as skill shortages, infrastructure gaps, and organizational resistance were prominent among moderate adopters, underscoring the need for targeted interventions. Larger organizations and those led by experienced managers demonstrated greater efficiency, highlighting the importance of resources and leadership in digital transformation. The study emphasizes the need for investment in infrastructure, workforce development, and tailored support for SMEs to unlock the full potential of big data. Future research should focus on longitudinal impacts, sector-specific challenges, and integrating emerging technologies such as AI and IoT. These findings provide actionable insights for policymakers and organizations to foster a data-driven, competitive, and inclusive digital economy in Ukraine