درخت حوزه‌های تخصصی

 This study aims to investigate the role of environmental concerns in green product purchasing decisions. The present study is in the field of applied research in terms of its purpose. In terms of nature and method, it is a descriptive-survey study. The statistical population of this study is all customers of Kalleh Co. products. Given that the research population is unlimited, a sample size of 384 people was selected using the Cochran formula and the available sampling method. Inferential statistics and the partial least squares (PLS) technique were also used to analyze the data and test the research hypotheses. The results revealed that willingness to pay more, awareness of green price, perceived green benefits, perceived green quality, and green foresight have a positive and significant effect on environmental concerns as well as green purchase decisions. In addition, it was found that environmental concerns have a positive and significant effect on green purchase intention. It was also found that green purchase intention has a positive and significant effect on green purchase decision. All hypotheses of this study were confirmed. Therefore, based on the results obtained, it seems that managers of commercial companies should pay more attention to the environmental concerns of their customers in order to improve the intention of customers to purchase green products.

  Background: Unmanned Aerial Vehicles (UAVs) utilizing and active interface with 5G networks has become the new frontier to tackling problems of latency and energy efficiency, interference, and resource management. Although prior researches explained the benefits of UAV integrated networks; overall assessment of various parameters and cases is still scarce. Objective: The article seeks to assess the performance of UAV integrated 5G network in terms of latency, power, signal quality, task coordination and coverage optimization and to ascertain the efficiency of optimization algorithms in the improvement of the integrated 5G network. Methods: Emulations were done in MATLAB and NS3 platforms in urban / suburban / emergency call settings. Latency, power consumption, SINR, and completion time were the performance indicator chosen in the paper. Optimization algorithms: Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), and the Multi-Objective Evolutionary Algorithm (MOEA) is evaluated in terms of Convergence time and Solution quality. Results : UAV-aided networks showed 36.7% and 29.2 % improvement in latency and energy consumption, while 33.6 % enhancement in SINR. MOEA offered the best results with 98.3% solution quality, and the PSO being the most convergence oriented. Minor deviations between simulation and real results highlight the need for adaptive mechanisms. Conclusion: The results presented focus on the enough potential of UAV-assisted 5G networks and their potential influence on improving performances in case of different criteria. Further research should focus on successfully implementing and deploying the proposed solutions and broadening the context of study to include 6G technologies.