This study investigates the potential predictors of resource creation behaviours in the Shanxi merchant courtyard scenic areas based on resource dependence theory. The research was conducted in China using questionnaire survey, and data analysis employed structural equation modelling, including mediation and moderation effects. The model was tested using a sample of 376 individual managers from scenic areas. The results show that external resource integration, internal resource integration, and shared value significantly affect resource creation in scenic areas. The findings indicate that shared value plays a significant mediating role in the relationship between resource integration and resource creation, while environmental dynamism significantly moderates this relationship. This study clearly demonstrates the relationship among resource integration, shared value, and value creation in scenic areas. This research contributes to the tourism management literature by identifying gaps and offering a comprehensive perspective to understand resource creation behaviours in the tourism industry.

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%.

Our intention in assembling this special issue of the Journal of Infrastructure, Policy and Development is to offer a state-of-the-art tour through the political economy issues associated with the provision of public infrastructure, and with the use of Public-Private Partnerships (PPPs) in particular. Anyone who is familiar with PPPs cannot fail to be impressed by the diversity of positions and claims regarding their properties. Some scholars maintain that PPPs are an efficient tool to enhance productivity due to their ability to manage demand-side risk. In contrast, other scholars see in PPPs a scheme whereby the public assumes the risk while the private partner takes the profit.

This paper is devoted to the discussion of dynamical properties of anisotropic dark energy cosmological model of the universe in a Bianchi type-V space time in the framework of scale covariant theory of gravitation formulated by Canuto et al.(phys.Rev.Lett.39:429,1977).A  dark energy cosmological model is presented by solving the field equations of this theory by using some physically viable conditions. The dynamics of the model is  studied  by computing the cosmological parameters, dark energy density, equation of state(EoS) parameter, skewness parameters, deceleration parameter and the jerk parameter. This being a scalar field model gives us the quintessence model of the universe which describes a significant dark energy candidate of our accelerating universe. All the physical quantities discussed are in agreement with the recent cosmological observations.