مطالب

: Background: Unmanned aerial vehicle (UAV) networks as an important part of the modern telecommunication are gaining importance in various situations, including rural coverage, urban settings and emergency situations. However, Interference, scalability and energy efficiency still pose problems to the advancement of wireless networks. Objective: The aim of current study is to integrate and assess an adaptive frequency management technique for improving the performance of communication networks involving UAVs in terms of interference, transmission rates, and reliability within different deployment settings. Methods: Experimental and simulated studies were performed to evaluate the effectiveness of the algorithm in this combination. Performance measurements in terms of latency, throughput, packet loss, energy consumption and signal strength were made under rural, urban and emergency conditions. The adaptable algorithm used certain working frequencies depending on the interferences present and the network performance parameters recorded. Results: The algorithm showed very distinct enhancements in all models and positions, decreasing latency by 20.5%, enhancing throughput by 14.5%, and decreasing the packet loss by 57.6% in the urban site settings. Other executed experiments documented improved energy efficiency and communication reliability in rural and emergency situations. Conclusion: The adaptive frequency management algorithm proposed by the authors effectively solves significant issues of critical concern in UAV networks while offering robust scalability for next-generation telecommunications infrastructure. The future research recommendation incorporates the combination of the proposed optimization with other complementary approaches and/or the testing of the developed system at more severe actual conditions.

Background: Telecommunications networks are exposed to numerous issues concerning equipment and that causes network outage, which proves very expensive. Basic maintenance methodologies like reactive or even scheduled preventive maintenance cannot cope up with the increasing trends in the facilities of telecom companies. Objective: The article examines how AI is applied to support predictive maintenance so that telecommunication networks can perform as intended with reduced downtime. Methods: The review of existing AI algorithms is presented, focusing on the ML models and deep learning methods. Network operations and maintenance logs are analyzed for data to assess the capabilities of the AI models in terms of prediction. It identifies and analyses such quantifiable parameters as the failure rate prediction accuracy and the response time cut. Results: Computerisation of the forecast maintenance revealed a corresponding decrease in equipment failure incidences and generally reduced time lost due to unscheduled stops. Through the improved network performance, the response to potential threats was quicker than before and services became more reliable and inexpensive to offer. Conclusion: To reduce network outages, reduce network vulnerability, and maximize the efficiency of telecommunications operations, the use of AI-based predictive maintenance can be viewed as a prospect. As technology advances, newer versions of AI algorithms will provide improved predictive strength and incorporation into the telecommunications system.

Although teacher-related factors have been regarded as significant elements in shaping the educational system, there is a scarcity of research regarding the relationship between teachers’ online knowledge sharing, commitment, and teachers’ self-efficacy. In this study, a quantitative correlational methodology and structural equation modeling were employed to address the research hypotheses. To achieve this, 113 English as a Foreign Language (EFL) teachers were selected through convenience sampling. Three questionnaires—namely, online knowledge sharing behavior, teaching commitment, and teachers' self-efficacy—were utilized to collect the data. The findings showed that knowledge sharing had a significant effect on teachers' commitment. Additionally, knowledge sharing had a significant effect on self-efficacy, which acted as a mediating variable. Furthermore, self-efficacy affected teachers' teaching commitment. Self-efficacy as a mediating variable strengthens the effect of knowledge sharing on teaching commitment. Overall, the findings supported the proposed hypothetical model of the variables. These results carry significance both theoretically and practically and provide valuable insights. 

This study applies Jacques Derrida’s (1976) deconstructive framework to examine the traditional supervision model at the Iran Language Institute (ILI), a leading EFL institution in Iran with 290 branches serving 1.2 million students annually. Through a qualitative case study employing Critical Discourse Analysis, it investigates how binary oppositions—supervisor/teacher, expert/novice, evaluation/development—and aporias, such as standardization versus individuality and professional growth versus punitive evaluation, sustain hierarchical power dynamics that constrain teacher agency and pedagogical innovation. Data from semi-structured interviews, classroom and feedback session observations, ILI policy documents, and teachers’ reflective journals reveal that unannounced observations and rigid rubrics prioritize compliance over creativity. This approach fosters teacher anxiety, performative teaching, and punitive outcomes, such as demotion to lower-level classes. Key contradictions include: (1) ILI’s mission of quality education versus evaluative supervision; (2) standardized methodologies versus diverse classroom needs; and (3) professional growth goals versus punitive evaluations. To address these issues, the study proposes three reforms: dialogic feedback to foster reciprocal dialogue, co-constructed evaluation criteria incorporating teachers’ contextual expertise, and Professional Learning Communities (PLCs) to promote peer-driven learning. By addressing a gap in Middle Eastern EFL supervision research, this study advances theoretical discourse on power dynamics in supervision and offers practical strategies for equitable, teacher-centered practices at ILI, with implications for global EFL contexts seeking transformative supervisory frameworks.

This study aimed to critically examine the representation of Iranian Islamic culture in English language textbooks, specifically the Vision series used in high schools, within Iran's socio-political context. Employing a mixed-methods approach, data were collected from 60 EFL teachers through questionnaires and semi-structured interviews. Analysis of textbook content revealed that while the series includes references to various Iranian ethnicities, there is a notable emphasis on regional representation, particularly in Vision 2. Quantitative findings indicated that teachers acknowledged the importance of integrating artistic and cultural elements of Iranian identity, supported by a statistically significant mean score reflecting their appreciation for such content. Qualitative insights highlighted diverse attitudes among teachers; some expressed satisfaction with cultural representation, whereas others emphasized the need for more Islamic elements. This variation corresponded with the educators' experience levels and familiarity with the curriculum. Overall, the study underscores that a balanced approach to English language instruction can enhance linguistic proficiency while fostering cultural identity, advocating for the development of educational materials that respect local cultural contexts and contribute to a meaningful language learning experience for Iranian students.