درخت حوزه‌های تخصصی

Background: The cloud-native architectures have reinvented the original strategies of the companies’ IT infrastructure approach and became popular due to the concepts of modularity, scalability, and resilience. These architectures respond to the shortcomings of the monolithic architectures to meet the new business challenges and workloads, including embracing innovation technologies like Artificial Intelligence and big data processing solutions. Objective: This study was designed with the objective of assessing the performance and business viability of cloud-native systems, based on critical indicators such as availability, resilience to failure, resource use, and compatibility with innovative technologies. The objective was to define the barriers and possibilities for improving cloud native architectures in various enterprises. Methods: A cross-sectional research, consideration, experiment test and case study and performance analysis. Response time, CPU and memory consumption and recovery time were compared across the range of throughput from 1000 to 12000 requests per second. To enhance the interpretational framework, key usage scenarios in the three sectors of healthcare, retail and finance were collected and compared with the results. Results: Cloud-native systems proved to provide high availability rates (> 99.9%), resource scalability, and component resource efficiency. With the use of AI in combination with big data analytics, improvement in performance was realized. But some of the problems that were seen include vendor lock, integration issues, and fluctuating peak load issues. Conclusion: All identified improvements signify the potential of cloud-native architectures for improving enterprise IT functioning. It is thus possible to continue perfecting the identified challenges to enhance their effectiveness, optimal for the current dynamic digital environment.

This mixed-methods study investigated the effect of the Memrise application on Iranian intermediate English as a Foreign Language (EFL) learners’ vocabulary learning and listening comprehension. The study involved 60 participants, with 30 in the experimental group using Memrise and 30 in the control group engaging in traditional textbook-based instruction. The data were collected through pre-tests and post-tests assessing vocabulary learning and listening comprehension, along with semi-structured interviews and classroom observations to capture learners’ experiences. Quantitative data were analyzed using independent samples t-tests, while qualitative data were analyzed via inductive coding with MAXQDA 20. The results showed significant improvements in both vocabulary learning and listening comprehension for the experimental group compared to the control group. Qualitative findings highlighted key features of Memrise, including its user-friendly interface, gamification, and authentic content, while also identifying challenges such as technical issues and limited vocabulary coverage. Learners expressed increased motivation, confidence, and enjoyment in using the app, though frustrations with technical glitches were noted. In addition, classroom observations supported these findings, revealing high levels of engagement and participation throughout the intervention. The study suggests that Memrise can be an effective tool for enhancing vocabulary and listening comprehension in EFL contexts, though technical and content limitations should be addressed for optimal learning experiences.