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%.

This study focused on the formulation and characterization of silver nanoparticles (AgNP) functionalized with d-limonene. The nanoparticles were functionalized by phase inversion and the synthesis of the nanoparticles was performed in situ; particle size was determined by laser diffraction, zeta potential and optical colloidal stability using Multiscan 20 for a period of 24 hours at 37 °C; the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the formulated material on Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 29213, Klebsiella oxytoca ATCC 700324, Enterococcus casseliflavus ATCC 700327, Escherichia coli BLEE, carbapenem-resistant Pseudomona aeruginosa were determined. The nanoparticles showed colloidal stability at a d-limonene concentration of 3.93%, silver ions at 1.61 × 10⁻³%, non-ionic adjuvant at 24% and ascorbic acid at 5.88%; citric acid/citrate (1:1) 0.48M for a pH of 4.5 was used as a buffer system. The formulation was classified as a polydisperse system (PD = 0.0851), with a zeta potential of −11.6 mV and average particle size of 81.5 ± 0.9 nm. A particle migration velocity of −0.199 ± 0.006 mm∙h⁻¹, a constant transmission profile and backscattering profile with variations of 10% were evidenced, which represents a stable formulation. The nanoparticles presented an MIC and an MBC of 28 μg∙mL⁻¹ (5.6 × 10⁻²% d-limonene and 4.7 × 10⁻⁵% AgNP) against all tested bacteria.

The impact of human activities on the quality of urban environment has become increasingly prominent and urban soil pollution problems on the health of local residents also gradually prominent. In addition, the study of heavy metal pollution in urban surface soil is an important part of the evolution model of urban geological environment so it is necessary to analyze the heavy metal pollution in urban soil. In this paper, the data of the given samples are processed and analyzed by MATLAB software and EXCEL spreadsheet. The three - dimensional image model and the planar model of metal element space are established by interpolation method. The spatial distribution of eight kinds of heavy metal elements in the city is presented in detail. For the urban environment, especially the macro-grasp of soil pollution, regulation provides a simple and accurate three-dimensional spatial distribution model of pollutants. Combined with data analysis of the urban area of different areas of heavy metal pollution to make a preliminary judgment. The data show that in the five types of cities, heavy soil pollution is the most serious in industrial areas. A method of imagination of the data analysis is boldly used and then combined with the distribution map, they found a source of pollution. For the spatial distribution of heavy metal elements, this paper uses EXCEL to calculate the data and MATLAB to map the data which showed a detailed and intuitive distribution map according to the distribution map can be analyzed in different areas of pollution; For the second question, this paper uses a method of design to deal with the data, part of the data for the results of the more effective show to determine the cause of pollution. For the third question, this article will be more serious pollution or a wider range of local screening, analysis, and then speculate the location of pollution sources. For other pollution information, this article is based on the modeling process encountered in the thought of the factors given.