This paper presents a comparative study of different methods for obtaining the dispersion curves of ultrasonic guided waves in anisotropic media. First, we present the classical algorithms used to find zeros and propose some improvements. Next, the spectral method is explained for modeling the guided waves in anisotropic materials while presenting a technique that can distinguish the modes present in the structure. The dispersion curves are plotted using a Matlab program and the results are compared with those of the DISPERSE software. In addition, a comparison with the results obtained by Nayfeh’s works in the field of Nondestructive testing by ultrasonic guided waves is included. Then a discussion is developed to highlight the strengths of the spectral method. For proper non-destructive testing, we need reliable information about the modes that propagate in our waveguide. Both analytical and spectral approaches have limitations in obtaining the exact displacement and stress profiles in a plate media. To remedy this, normalization by the acoustic power is essential. Next, the displacement and stress fields obtained from the spectral method of the modes that can propagate in the plate are compared to those obtained analytically. A very good concordance is then noticed. Based on the results obtained, the spectral method presents a very good alternative for obtaining dispersion curves. It is a convergent method, stable, easy to implement with a very low calculation time.

In this paper, we introduce the notion of normalized distance Laplacian matrices for signed graphs corresponding to the two signed distances defined for signed graphs. We characterize balance in signed graphs using these matrices and compare the normalized distance Laplacian spectral radius of signed graphs with that of all-negative signed graphs. Also we characterize the signed graphs having maximum normalized distance Laplacian spectral radius.

BACKGROUND AND OBJECTIVES: Urban heat island is characterized by higher temperatures in urban areas compared to their surroundings. Vegetation, quantified by the normalized difference vegetation index, is key in mitigating urban heat island effects and influencing land surface temperature. With the rise of Machine Learning techniques, particularly random forest, land surface temperature predictions have become more accurate. This study explores alternative normalized difference vegetation index adjustments to understand their impact on urban heat island classification in Chiang Mai, Thailand. It investigates how changes to the normalized difference vegetation index can help to be part of practical urban planning measures, such as prioritizing vegetation type and location for cooling strategies in urban areas. Furthermore, the study aims to highlight the importance of vegetation as a sustainable solution for mitigating the adverse effects of urban heat island and enhancing urban livability.METHODS: Satellite data from Sentinel-2 and Landsat 8 for 2016–2022 were used to develop a 20-meters grid resolution dataset, resulting in approximately 2 million points. Random Forest was employed to predict land surface temperature, followed by systematically adjusting normalized difference vegetation index values from -100 percent to +100 percent in 10 percent increments. Urban heat island was classified based on standard deviation thresholds. The results were analyzed and compared visually using geographic information system, incorporating spatial variations and heat intensity patterns to better understand the urban landscape.FINDINGS: Adjusting normalized difference vegetation index values showed a nonlinear relationship with Land Surface Temperature predictions, where certain thresholds caused unexpected decreases in Land Surface Temperature. Urban heat island classifications identified distinct urban regions with varying heat intensities. The visual comparison highlighted significant differences between the base case and alternative scenarios, revealing the sensitivity of land surface temperature to vegetation density. the results also emphasized the role of high normalized difference vegetation index values in cooling urban regions and reducing urban heat island intensity, while extreme reductions in vegetation led to potential misclassification of water bodies, creating anomalies in cooling patterns. The results of this research provide information on important variables that affect the changes in the urban heat islands, focusing on changes in vegetation, which can be a part of decision-making to improve urban planning in the future.CONCLUSION: The study demonstrates the influence of normalized difference vegetation index on urban heat island classification and its potential in urban planning strategies. By highlighting nonlinear trends, the research underscores further the need to explore vegetation dynamics in land surface temperature predictions. The findings contribute to a deeper understanding of urban heat island effects and provide a basis for enhancing machine Learning models and urban planning frameworks. Future studies could expand to other urban areas, incorporate additional variables, and refine predictive algorithms for broader applications. This study will serve as a foundation for the development of future real-time monitoring tools that will enable proactive and sustainable solutions to UHI problems. 

A pressure-based implicit procedure to solve the Euler and Navier-Stokes equations on a nonorthogonal mesh with collocated finite-volume formulation is described. The boundedness criteria for this procedure are determined from the NVD method, and are applied to the convected quantities. The procedure incorporates the k −ε eddy viscosity turbulence model. The algorithm is tested for several inviscid flows at different Mach numbers ranging from subsonic to supersonic on a bump in channel geometry where the results are compared with other existing numerical solutions. The method is then validated against experimental data for the case of turbulent transonic flow through a gas turbine rotor blade cascade. The comparisons presented show that the quality of the resolution of the SBIC scheme for steady state flows is remarkable.

Nowadays, because of the growing numbers of vehicles on streets and roads, the use of intelligent control systems to improve driving safety and health has become a necessity. To design and implement such control systems, having information about traffic light colors is essential. There are the wide variety of traffic lights in terms of light intensity and color. Therefore it seems that design and practical implementation of these systems with acceptable performance is difficult. The study has been discussed extracting, Categories and the offer of a specific model for color and intensity of traffic signals based on an improved algorithm. The proposed intelligent system will detect traffic lights through images by installing camera instead of using electronic sensors. After capturing, the image sequence will then be analyzed using computer based programs for extracting of lights specifications.