In the domains of geological study, natural resource exploitation, geological hazards, sustainable development, and environmental management, lithological mapping holds significant importance. Conventional approaches to lithological mapping sometimes entail considerable effort and difficulties, especially in geographically isolated or inaccessible regions. Incorporating geological surveys and satellite data is a powerful approach that can be effectively employed for lithological mapping. During this process, contemporary RS-enhancing methodologies demonstrate a remarkable proficiency in identifying complex patterns and attributes within the data, hence facilitating the classification of diverse lithological entities. The primary objective of this study is to ascertain the lithological units present in the western section of the Sohag region. This objective will be achieved by integrating Landsat ETM⁺ satellite imagery and field observations. To achieve our objectives, we employed many methodologies, including the true and false color composition (FCC&TCC), the minimal noise fraction (MNF), principal component analysis (PCA), decoration stretch (DS), and independent component analysis (ICA). Our findings from the field investigation and the data presented offer compelling evidence that the distinct lithological units can be effectively distinguished. A recently introduced geology map has been incorporated within the research area. The sequence of formations depicted in this map is as follows: Thebes, Drunka, Katkut, Abu Retag, Issawia, Armant, Qena, Abbassia, and Dandara. Implementing this integrated technique enhances our comprehension of geological units and their impacts on urban development in the area. Based on the new geologic map of the study area, geologists can improve urban development in the regions by detecting building materials “aggregates”. This underscores the significance and potential of our research in the context of urban development.

Projects implemented under life cycle contracts have become increasingly common in recent years to ensure the quality of construction and maintenance of energy infrastructure facilities. A key parameter for energy facility construction projects implemented under life cycle contracts is their duration and deadlines. Therefore, the systematic identification, monitoring, and comprehensive assessment of risks affecting the timing of work on the design and construction is an urgent practical task. The purpose of this work is to study the strength of the influence of various risks on the duration of a project implemented on the terms of a life cycle contract. The use of the expert assessment method allows for identifying the most likely risks for the design and construction phases, as well as determining the ranges of deviations from the baseline indicator. Using the obtained expert evaluations, a model reflecting the range and the most probable duration of the design and construction works under the influence of risk events was built by the Monte-Carlo statistical method. The results obtained allow monitoring and promptly detecting deviations in the actual duration of work from the basic deadlines set in the life cycle contract. This will give an opportunity to accurately respond to emerging risks and build a mutually beneficial relationship between the parties to life cycle contracts.

Under the developing trend of artificial intelligence (AI) technology gradually penetrating all aspects of society, the traditional language education industry is also greatly affected ^[1]. AI technology has had a positive impact on college English teaching, but it also presents challenges and negative impacts. On the positive side, AI technology can provide personalized learning experiences, real-time feedback, and autonomous learning opportunities, and so on. However, it may also lead to a lack of communication between students and humans, resulting in a decline in students’ interpersonal skills, and cause students’ dependence on online learning resources as well as possible risks to student data privacy and security, and other negative impacts. To address these challenges, teachers can adopt the following countermeasures: improving teachers’ skills in the use of AI technology incorporated in the classroom, offering personalized instruction to reduce students’ dependence on AI technologies, emphasizing the cultivation of students’ humanistic literacy and interpersonal communication ability. Additionally, colleges and technology providers should strengthen data security and privacy protection to ensure the safety and confidentiality of student data. By implementing comprehensive measures, we can maximize the advantages of AI technology in college English teaching while overcoming potential issues and challenges.

This study investigates seismic risk and potential impacts of future earthquakes in the Sunda Strait region, known for its susceptibility to significant seismic events due to the subduction of the Indo-Australian Plate beneath the Eurasian Plate. The aim is to assess the likelihood of major earthquakes, estimate their impact, and propose strategies to mitigate associated risks. The research uses historical seismic data and probabilistic models to forecast earthquakes with magnitudes ranging from 6.0 to 8.2 Mw. The Gutenberg-Richter model helps project potential earthquake occurrences and their impacts. The findings suggest that the probability of a major earthquake could occur as early as 2026–2027, with a more significant event estimated to likely occur around 2031. Economic estimates for a 7.8–8.2 Mw earthquake suggest potential damage of up to USD 1.255 billion with significant loss of life. The study identifies key vulnerabilities, such as inadequate building foundations and ineffective disaster management infrastructure, which could worsen the impact of future seismic events. In conclusion, the research highlights the urgent need for comprehensive seismic risk mitigation strategies. Recommendations include reinforcing infrastructure to comply with seismic standards, implementing advanced early warning systems, and enhancing public education on earthquake preparedness. Additionally, government policies must address these issues by increasing funding for disaster management, enforcing building regulations, and incorporating traditional knowledge into construction practices. These measures are essential to reducing future earthquake impacts and improving community resilience.

Objective: This study investigates the influence of tobacco prices on consumption habits among 200 smokers in Lisbon, Portugal, focusing on generational preferences and perceptions of tobacco taxes. Methods: A cross-sectional survey was conducted using a quantitative approach. Participants were categorized by generational cohort, and data on tobacco consumption types, awareness of tobacco taxes, and opinions on tax increases were collected. Statistical analyses were used to explore relationships and predictors. Results: Findings reveal that 46.5% of participants favored heated tobacco, 37.0% smoked cigarettes, and 16.5% consumed other types like roll-your-own tobacco and electronic cigarettes. Significant generational differences were observed, with older cohorts predominantly smoking cigarettes (51.5%), while Generation Z showed a preference for heated tobacco (55.2%). Most smokers (79.5%) were aware of tobacco taxes, with diverse opinions on their purpose: revenue generation (44.0%), consumption reduction (44.5%), and indifference (11.5%). Despite this awareness, 60.5% reported no change in consumption habits due to tax increases. Conclusion: The study underscores the complexity of tobacco consumption behaviors influenced by price and generational factors. While heated tobacco gains popularity among younger smokers, traditional cigarettes remain prevalent among older cohorts. The findings highlight challenges in tobacco control policies, suggesting a need for comprehensive strategies integrating price measures with targeted educational and cessation interventions to effectively reduce tobacco use across different generations.

In recent years, how farmers leverage social capital to improve their well-being has become a crucial question in post-poverty alleviation China. This study assessed the impact of ‘linking social capital’ on farmers’ well-being, as mediated by self-efficacy. The study was conducted using data collected from 443 randomly selected farmers from two villages in Guizhou Province, China. The Partial Least Squares Structural Equation Model (PLS-SEM) was employed to analyze the proposed relationships in the study. The results indicate that linking social capital, when mediated by self-efficacy, positively impacted farmers’ well-being. This suggests that policymakers and implementers exercising hierarchical power in social improvement programs in disadvantaged provinces, such as Guizhou, should take full advantage of linking social capital to effectively improve farmers’ well-being. In doing so, the study concludes, they should consider the positive role farmers’ self-efficacy can play in the process.