This paper uses quantitative research methods to explore the differences in the impact of virtual influencers on different consumer groups in the context of technological integration and innovation. The study uses DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering technology to segment consumers and combines social media behavior analysis with purchase records to collect data to identify differences in consumer behavior under the influence of virtual influencers. Consumers' emotional resonance and brand awareness information about virtual influencers are extracted through sentiment analysis technology. The study finds that there are significant differences in the influence of virtual influencers on different consumer groups, especially in high-potential purchase groups, where the influence of virtual influencers is strong but short-lived. This paper further explores the deep integration of virtual influencer technology with new generation information technologies such as 5G and artificial intelligence, and emphasizes the importance of such technological integration in enhancing the endogenous and empowering capabilities of virtual influencers. The research results show that technological integration and innovation can not only promote the development of virtual influencers, but also provide new technical support for infrastructure construction, especially in the fields of smart cities and industrial production. This paper provides a new theoretical perspective for the market application of virtual influencers and provides practical support for the application of virtual technology in infrastructure construction.

In developing countries, urban mobility is a significant challenge due to convergence of population growth and the economic attraction of urban centers. This convergence of factors has resulted in an increase in the demand for transport services, affecting existing infrastructure and requiring the development of sustainable mobility solutions. In order to tackle this challenge, it is necessary to create optimal services that promote sustainable urban mobility. The main objective of this research is to develop and validate a comprehensive methodology framework for assessing and selecting the most sustainable and environmentally responsible urban mobility services for decision makers in developing countries. By integrating fuzzy multi-criteria decision-making techniques, the study aims to address the inherent complexity and uncertainty of urban mobility planning and provide a robust tool for optimizing transportation solutions for rapid urbanization. The proposed methodology combines three-dimensional fuzzy methods of type-1, including AHP, TOPSIS and PROMETHEE, using the Borda method to adapt subjectivity, uncertainty, and incomplete judgments. The results show the advantages of using integrated methods in the sustainable selection of urban mobility systems. A sensitivity analysis is also performed to validate the robustness of the model and to provide insights into the reliability and stability of the evaluation model. This study contributes to inform decision-making, improves policies and urban mobility infrastructure, promotes sustainable decisions, and meets the specific needs of developing countries.

In this study, we consider the extended Brinkman's-Darcy model for a triple diffusive convection system which consists of some parameters such as Taylor number (Ta), Solutal Rayleigh numbers (RC1 , RC2 ), and Prandtl number (Pr). To investigate the range of these parameters, a dynamical system of the Ginzburg-Landau equation is developed. The parametric analysis and comparative study of the model for the three Rayleigh numbers which leads to the clear fluid layer, sparsely packed porous layer, and densely packed porous layer is done with the help of bifurcation maps and the Lyapunov exponents. It is found that for a certain range of parameters, the system exhibits a chaotic behaviour.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

The role of trace gases in the storage of heat in the atmosphere of the Earth and in the exchange of energy between the atmosphere and outer space is discussed. The molar heat capacities of the trace gases water vapor, carbon dioxide and methane are only slightly higher than those of nitrogen and oxygen. The contribution of trace gases carbon dioxide and methane to heat storage is negligible. Water vapor, with its higher concentration and conversion energies, contributes significantly to the heat storage in the atmosphere. Most of the heat in the Earth’s atmosphere is stored in nitrogen and oxygen, the main components of the atmosphere. The trace gases act as converters of infrared radiation into heat and vice versa. They are receivers and transmitters in the exchange of energy with outer space. The radiation towards space is favored compared to the reflection towards the surface of the Earth with increasing altitude by decreasing the density of the atmosphere and condensation of water vapor. Predictions of the development of the climate over a century by extrapolation are critically assessed.

Given the increasing demand for sustainable energy sources and the challenges associated with the limited efficiency of solar cells, this review focuses on the application of gold quantum dots (AuQDs) in enhancing solar cell performance. Gold quantum dots, with their unique properties such as the ability to absorb ultraviolet light and convert it into visible light expand the utilization of the solar spectrum in solar cells. Additionally, these quantum dots, through plasmonic effects and the enhancement of localized electric fields, improve light absorption, charge carrier generation (electrons and holes), and their transfer. This study investigates the integration of quantum dots with gold plasmonic nanoparticles into the structure of solar cells. Experimental results demonstrate that using green quantum dots and gold plasmonic nanoparticles as intermediate layers leads to an increase in power conversion efficiency. This improvement highlights the significant impact of this technology on solar cell performance. Furthermore, the reduction in charge transfer resistance and the increase in short-circuit current are additional advantages of utilizing this technology. The findings of this research emphasize the high potential of gold quantum dots in advancing next-generation solar cell technology.