Accurate temperature control during the induction heating process of carbon fiber reinforced polymer (CFRP) is crucial for the curing effect of the material. This paper first builds a finite element model of induction heating, which combines the actual fiber structure and resin matrix, and systematically analyzes the heating mechanism and temperature field distribution of CFRP during the heating process. Based on the temperature distribution and variation observed in the material heating process, a PID control method optimized by the sparrow search algorithm is proposed, which effectively reduces the temperature overshoot and improves the response speed. The experiment verifies the effectiveness of the algorithm in controlling the temperature of the CFRP plate during the induction heating process. This study provides an effective control strategy and research method to improve the accuracy of temperature control in the induction heating process of CFRP, which helps to improve the results in this field.

The purpose of this study is to predict the frequency of mortality from urban traffic injuries for the most vulnerable road users before, during and after the confinement caused by COVID-19 in Santiago de Cali, Colombia. Descriptive statistical methods were applied to the frequency of traffic crash frequency to identify vulnerable road users. Spatial georeferencing was carried out to analyze the distribution of road crashes in the three moments, before, during, and after confinement, subsequently, the behavior of the most vulnerable road users at those three moments was predicted within the framework of the probabilistic random walk. The statistical results showed that the most vulnerable road user was the cyclist, followed by motorcyclist, motorcycle passenger, and pedestrian. Spatial georeferencing between the years 2019 and 2020 showed a change in the behavior of the crash density, while in 2021 a trend like the distribution of 2019 was observed. The predictions of the daily crash frequencies of these road users in the three moments were very close to the reported crash frequency. The predictions were strengthened by considering a descriptive analysis of a range of values that may indicate the possibility of underreporting in cases registered in the city’s official agency. These results provide new elements for policy makers to develop and implement preventive measures, allocate emergency resources, analyze the establishment of policies, plans and strategies aimed at the prevention and control of crashes due to traffic injuries in the face of extraordinary situations such as the COVID-19 pandemic or other similar events.

This study delves into the evolving landscape of smart city development in Kazakhstan, a domain gaining increasing relevance in the context of urban modernization and digital transformation. The research is anchored in the quest to understand how specific technological factors influence the formation of smart cities within the region. To this end, the study adopts a Spatial Autoregressive Model (SAR) as its core analytical tool, leveraging data on server density, cloud service usage, and electronic invoicing practices across various Kazakhstani cities. The crux of the research revolves around assessing the impact of these selected technological variables on the smart city development process. The SAR model’s application facilitates a nuanced understanding of the spatial dynamics at play, offering insights into how these factors vary in influence across different urban areas. A key finding of this investigation is the significant positive correlation between the adoption of electronic invoicing and smart city development, a result that stands in contrast to the relatively insignificant impact of server density and cloud service usage. The conclusion drawn from these findings underscores the pivotal role of digital administrative processes, particularly electronic invoicing, in driving the smart city agenda in Kazakhstan. This insight not only contributes to the academic discourse on smart cities but also holds practical implications for policymakers and urban planners. It suggests a strategic shift towards prioritizing digital administrative innovations over mere infrastructural or technological upgrades. The study’s outcomes are poised to guide future smart city initiatives in Kazakhstan and offer a reference point for similar emerging economies embarking on their smart city journeys.

With the increasing call for sustainable development, cities’ demand for green innovation has also been growing. However, relatively little research summarizes the influencing factors of urban green innovation. In this study, we conducted a visual analysis of 1193 research articles on green innovation in cities from the Web of Science core database using bibliometrics and visualization analysis. By analyzing co-occurrence, co-citation, and high-frequency keywords in the literature, we explored the current research status and development trends of influencing factors of urban green innovation and summarized the research in this field. The study found that collaboration among authors and institutions in this field needs to be strengthened to a certain extent. In addition, the study identified the research hotspots and frontiers in the field of urban green innovation, including “management”, “diffusion”, “smart city”, “indicator”, “sustainable city”, “governance”, and “environmental regulation”. Among them, “management”, “governance”, “indicator”, and “internet” are the research frontiers in this field, which are expected to have profound impacts on the future development of urban green innovation. The co-citation analysis results found that China has the highest research output in this field, followed by the United States, England, Australia, and Italy. In conclusion, this study uses CiteSpace software to identify important influencing factors and development trends of urban green innovation. Urban green innovation has gradually become a norm for social and collective behavior in the process of concretization, interdisciplinary development, and technological innovation. These findings have important reference value for promoting research and practice of urban green innovation.

Taxus cuspidata Sieb. ET. Zucc. is a taxus of Taxaceae, a rare third-order relict species distributed in northeastern China, and a wild endangered plant species protected by national level I. Taxol (paclitaxel, trade name taxol) and cephalomannine (cephalomannine) are all diterpenoids contained in the genus Taxus, with broad-spectrum anti-tumor activity and unique anti-cancer mechanism. In this study, the distribution of paclitaxel and cephalomannine in the leaves of Taxus cuspidata in different parts and different growth stages was discussed. The results showed that the content of two substances in the leaves of the majority of the crowns was lower than that of the biennial and tertiary there were no significant differences in the contents of two substances in the two-year and three-year-old

foliage. There was no significant difference in the contents of the two layers in the three levels of the noodles, and

the content of the male was slightly higher than that of the dark. The content of paclitaxel in the leaves of natural

northeast yew was the highest at dormancy period, and the content of flowering and fruit was not much different. The

content of Cephalotaxin was the highest in dormancy period, and that of cephalosporin the content of paclitaxel and

cephalomannine in each plant were significantly different. There was significant difference between the two plants.

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.