درخت حوزه‌های تخصصی

In today's competitive markets, brands are always trying to create a desirable image of their products and services to elicit more favorable tendencies in customers. Beyond the marketing efforts of brands, there is an important factor that can either strengthen or weaken these marketing efforts. This factor is related to the country of origin or manufacturer of a brand, which can have an impact on consumer behavior and perceptions of products and brands from that country. Therefore, the aim of this research is to investigate the effect of the country of origin and manufacturer on consumer perceptions of the institutional environment of countries and purchasing decisions, taking into account the moderating role of beliefs arising from customer trust. This research is an applied study and, in terms of data collection method, is descriptive-correlational. A library approach was used to extract studies, and a field approach was used to collect statistical data, with the data collection tool being a questionnaire. The study population consisted of customers who own and purchase iPhones from the Apple brand in districts 1, 2, and 3 of Tehran. Descriptive statistics and inferential analysis were used to analyze the data, and the software used to analyze the data was SPSS and smart PLS. The results indicate that the country of origin of a brand has an impact on perceptions of the legitimacy and regulatory environment of that country. Additionally, the results show that the country of manufacture of a brand also has an impact on perceptions of the normative environment and regulatory environment of that country. On the other hand, perceptions of the legitimacy of the country of manufacture of a brand have an impact on customer preferences for a brand and a willingness to pay a higher price for a brand. However, the impact of the country of origin on customer purchasing decisions with the moderating role of beliefs in brand benevolence, honesty, and capability has not been confirmed.

Getting highly accurate output in biomedical data processing concerning biomedical signals and images is impossible because biomedical data are generated from various electronic and electrical resources that can deliver the data with noise. Filtering is widely used for signal and image processing applications in medical, multimedia, communications, biomedical electronics, and computer vision. The biggest problem in biomedical signal and image processing is developing a perfect filter for the system. Digital filters are more advanced in precision and stability than analog filters. Digital filters are getting more attention due to the increasing advancements in digital technologies. Hence, most medical image and signal processing techniques use digital filters for preprocessing tasks. This paper briefly explains various filters used in medical image and signal processing. Matlab is a famous mathematical, analytical software with a platform and built-in tools to design filters and experiment with different inputs. Even though this paper implements filters like, Mean, Median, Weighted Average, Guassian, and Bilateral in Python to verify their performance, a suitable filter can be selected for biomedical applications by comparing their performance.