With fresh bitter melon and green tea as main ingredients, xylitol, sucrose and citric acid as auxiliary ingredients, a new cool health tea beverage was developed. The optimum formula of low sugar bitter melon green tea compound beverage was developed by single factor experiment and orthogonal test based on sensory evaluation. The results showed that the optimum formula was as follows: Bitter melon juice was added at 7%, green tea extract was added at 20%, total xylitol and sucrose was 6% (mass ratio 1:1), citric acid was added at 0.2%, and the volume was fixed to 100% with deionized water. The product has light green color, harmonious aroma, moderate acidity and sweetness, and clear texture. The aftertaste is long, with tea polyphenol content of 342 mg/kg, soluble solids of 5.2% and pH 5.8.

The heat collection evaporator was modeled based on equilibrium homogeneous theory, and the Runge-Kutta calculation method was used to analyze and solve the flow in the heat collection evaporator. The influence of environmental factors such as solar irradiance, ambient temperature and wind speed on the variation of refrigerant pressure in two kinds of heat collecting evaporator was analyzed under the set working conditions. The results show that the solar energy irradiance has a great influence on the pressure drop in the tube of serpentine heat collecting evaporator, and the maximum pressure drop of the refrigerant in the tube is 16.3%, minimum pressure drop is 7.8%. However, it has little influence on the pressure drop of the tube sheet evaporator. The maximum pressure drop in the refrigerant tube of the tube sheet evaporator is 4.8%, minimum pressure drop is 1.8%. When the irradiance reaches 800 W/m², the refrigerant in the serpentine-tube evaporator has been completely vaporized at 6 m, it’s completely vaporized at 3 m.

Land use changes have been demonstrated to exert a significant influence on urban planning and sustainable development, particularly in regions undergoing rapid urbanization. Tehran Province, as the political and economic capital of Iran, has undergone substantial growth in recent decades. The present study employs sophisticated Geographic Information System (GIS) instruments and the Google Earth Engine (GEE) platform to comprehensively track and analyze land use change over the past two decades. A comprehensive analysis of Landsat images of the Tehran metropolitan area from 2003 to 2023 has yielded significant insights into the patterns of land use change. The methodology encompasses the utilization of GIS, GEE, and TerrSet techniques for image classification, accuracy assessment, and change detection. The Kappa coefficients for the maps obtained for 2016 and 2023 were 0.82 and 0.87 for four classes: built-up, vegetation cover, barren land, and water bodies. The findings suggest that, over the past two decades, Tehran Province has undergone a substantial decline in ecological and vegetative areas, amounting to 2.4% (458.3 km²). Concurrently, the urban area and the barren lands have expanded by 287.5 and 125.5 km², respectively. The increase in water bodies during this period is likely attributable to the reduction of vegetation cover and dam construction in the region. The present study demonstrates that remote sensing and GIS are excellent tools for monitoring environmental and sustainable urban development in areas experiencing rapid urbanization and land use changes.

Artificial intelligence (AI) has rapidly evolved, transforming industries and addressing societal challenges across sectors such as healthcare and education. This study provides a state-of-the-art overview of AI research up to 2023 through a bibliometric analysis of the 50 most influential papers, identified using Scopus citation metrics. The selected works, averaging 74 citations each, encompass original research, reviews, and editorials, demonstrating a diversity of impactful contributions. Over 300 contributing authors and significant international collaboration highlight AI’s global and multidisciplinary nature. Our analysis reveals that research is concentrated in core journals, as described by Bradford’s Law, with leading contributions from institutions in the United States, China, Canada, the United Kingdom, and Australia. Trends in authorship underscore the growing role of generative AI systems in advancing knowledge dissemination. The findings illustrate AI’s transformative potential in practical applications, such as enabling early disease detection and precision medicine in healthcare and fostering adaptive learning systems and accessibility in education. By examining the dynamics of collaboration, geographic productivity, and institutional influence, this study sheds light on the innovation drivers shaping the AI field. The results emphasize the need for responsible AI development to maximize societal benefits and mitigate risks. This research provides an evidence-based understanding of AI’s progress and sets the stage for future advancements. It aims to inform stakeholders and contribute to the ongoing scientific discourse, offering insights into AI’s impact at a time of unprecedented global interest and investment.