This study aims to explore the urban resilience strategies and public service innovations approaches adopted by the Shanghai Government in response to COVID-19 pandemic. The study utilized a combination of primary and secondary data sources, such as government reports, policy documents, and interviews with important individuals involved in the matter. The current research focused on qualitative data and examined the different aspects resilience, including infrastructure, economy, society, ecology, and organizations. The findings indicate that infrastructure resilience plays a crucial role in maintaining the stability and dependability of essential public facilities, achieved through online education and intelligent transportation systems. Implementing rigorous waste management and pollution control measures with a focus on ecological resilience has significantly promoted environmentally sustainable development. Shanghai city has achieved economic resilience by stabilizing its finances and providing support to businesses through investments in research, technology and education. Shanghai city has enhanced its organizational resilience by fostering collaboration across several sectors, bolstering emergency management tactics and enhancing policy execution.

Purpose: This study aimed to explore the perception types of workplace spirituality among nurses. Method: To achieve this, Q methodology was applied, selecting 34 Q samples from a total of 102 Q statements extracted. The Q samples were distributed among 40 nurses and categorized into a normal distribution. A 9-point scale was used for measurement, and the data were analyzed using the pc-QUANL program. Results: The four types identified were ‘reflective type’, ‘nursing-oriented type’, ‘relationship-oriented type’, and ‘spirituality-oriented type’. Conclusion: The four types derived in this study classify nurses’ perceptions of workplace spirituality for establishing a nurse’s workplace spirituality that provides integrated nursing care. This categorization can serve as foundational information when planning workplace spirituality programs, considering each type’s characteristics.

The paper proposes a methodology for the analysis and evaluation of the traffic scheme of Bulgarian cities. The authors combine spatial, network, and socio-economic analyses of cities with transport operators’ financial-economic evaluation, sociological studies of transport habits, and the possibilities of new information technologies for transport modeling (such as geographic information systems). The model proposes several approaches to optimize the municipality’s transport scheme. It results from a new need to improve urban traffic, the quality of transport services, and the integration of urban transport into the regional economy of Stara Zagora municipality. It presents a description, analysis, and outline of the opportunities for developing urban transport connectivity and mobility in Stara Zagora municipality. The research results show a deficit of transport connectivity between the different parts of the city, reflecting on the regional economy’s development and the efficiency of the environment and the population.

This paper analyzes the characteristics and influence mechanisms of financial support for China’s strategic emerging industries. Using a sample of 356 listed companies across nine major industries, we conduct an in-depth analysis of the efficiency of financial support and its influencing factors. In addition, this paper analyzes the influence mechanism of financial support for strategic emerging industries based on the relevant theory of financial support for industry development. It clarifies the internal and external influencing factors. Based on the theoretical analysis, a two-stage empirical investigation was conducted: The data of 356 listed companies in strategic emerging industries from 2010 to 2022 were selected as a sample, and the data envelopment analysis (DEA) method was applied to measure efficiency. The influencing factors were then analyzed using a Tobit regression and an intermediate effects test.

Our study evaluated the effect of vanadium (V) on the behavior of Zinnia elegans “double variegated”. In this experiment, Zinnia plants grown in a greenhouse were fed with a nutrient solution and two concentrations of vanadium (0, 6, and 10 μm) applied four times during the experiment. The V at its levels of 6 µm and 10 µm increased plant length, number of inflorescences and fresh weight. We observed that during the development and appearance of flower buds, and flowering were earlier with the addition of 6 µm and 10 µm. During harvest the changes in size and shape were homogeneous with the control treatment. With the addition of 6 µm, flowers of different sizes were induced, with non-uniform petals, but with different shades of color. With 10 µm the shape of the petals, the distance between them and changes in the shades of the flowers were modified. The postharvest life for the flowers of the control treatment was shorter (15 days), the petals, anthers and floral disc at this time were observed in a poor condition. While 6 µm and 10 µm had a longer postharvest life (20 days), the flowers had a good presentation, their colors were more intense compared to the harvest stage. The application of this beneficial element contributed to the development and flowering of Zinnia in the greenhouse. It is suggested that future research be carried out on the accumulation and/or concentration of vanadium in the different stages of growth or its effect on the concentration of other nutrients.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.