The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI's capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

The fields of urban design and public health play an important role in the success and failure of the city. Research combining the two fields to improve citizens’ lives is rare, particularly in a rapidly developing city like Doha. This study highlights the urban planning efforts of the municipality of Doha city to improve the mental health of its citizens and aims to understand the impact of urban design elements on mental health by analyzing the physical form and characteristics of green urban areas in Doha. The methods employed include an observational study and a structured survey interview, where visitors’ responses to selected green urban areas are analyzed. The results show how Doha officials are working to improve the mental health of its citizens by providing accessible, child-friendly, older citizen-friendly, and equitable green urban spaces and suggesting policies that could provide more opportunities for people and the government to provide a healthier environment in Doha. The implications encompass shaping urban design strategies, raising public awareness, enhancing healthcare initiatives, and ultimately emphasizing the positive impact of well-designed green spaces for mental well-being in Doha and other Gulf countries.

A gradually detailed geophysical investigation took place on Ancient Marina territory. In that area was extended Ancient Tritaea, according to responsible Archaeological Services. The first approach had been attempted since 1988 by applied electric mapping based on a twin-probe array. Later, the survey extended to the peripheral zone under the relative request from the 6th Archaeological Antiquity. A new approach was implemented by combining three different geophysical techniques, like electrical mapping, total intensity, and vertical gradient. These were applied on discrete geophysical grids. Electric mapping tried to separate the area into low and high-interest subareas according to soil resistance allocation. That technique detected enough geometrical characteristics, which worked as the main lever for the application of two other geophysical techniques. The other two techniques would be to certify the existence of geometrical characteristics, which divorced them from geological findings. Magnetic methods were characterized as a rapid technique with greater sensitivity in relation to electric mapping. Also, vertical gradient focuses on the horizontal extension of buried remains. Processing of magnetic measurements (total and vertical) certified the results from electric mapping. Also, both of the techniques confirmed the existence of human activity results, which were presented as a cross-section of two perpendicular parts. The new survey results showed that the new findings related to results from the previous approach. Geophysical research in that area is continuing.

Renewable energy is gaining momentum in developing countries as an alternative to non-renewable sources, with rooftop solar power systems emerging as a noteworthy option. These systems have been implemented across various provinces and cities in Vietnam, accompanied by government policies aimed at fostering their adoption. This study, conducted in Ho Chi Minh City, Vietnam investigates the factors influencing the utilization of rooftop solar power systems by 309 individuals. The research findings, analyzed through the Partial least squares structural equation modeling (PLS-SEM) model, reveal that policies encouragement and support, strategic investment costs, product knowledge and experience, perceived benefits assessment, and environmental attitudes collectively serve as predictors for the decision to use rooftop solar power systems. Furthermore, the study delves into mediating and moderating effects between variables within the model. This research not only addresses a knowledge gap but also furnishes policymakers with evidence to chart new directions for encouraging the widespread adoption of solar power systems.

Enhancing the emphasis on incorporating sustainable practices reinforces a linear transition towards a circular economy by organizations. Nevertheless, although studies on circular economy demonstrate an increasing trend, the drivers that support circular economy practices towards sustainable business performance in the Small and Medium-Sized Enterprise (SME) sector, especially in developing nations, demand exploration. Accordingly, the study examines circular economy drivers, i.e., green human resource management, in establishing sustainability performance and environmental dynamism as moderating variables. The study engaged 207 SMEs and 621 respondents who were analyzed utilizing structural equation modeling. The analysis indicated that sustainable business performance was affected by green human resource management and a circular economy. Subsequently, the circular economy mediated the linkage between green human resources management and sustainable business performance. The environmental dynamism moderated the linkage between green human resources management and the circular economy.