مطالب

  Background: The marriage of 5G and Artificial Intelligence (AI) has been brought forward as a key enabler of Industry 4.0 and smart city applications. These technologies solve the problem of latency, scalability, and energy use, providing technology support for real-time decision-making and efficient organization of work. Nevertheless, studies regarding their individual and collective effects in a plethora of industrial and urban contexts are still limited. Objective: The objective of this research is to assess the performance, energy saving, and expansibility of 5G and AI synergies in manufacturing, logistics, healthcare, and smart city applications and highlight their challenges and potential for further exploration. Methods: An experimental data collection, mathematical modeling and comparative analysis approach was employed. Performance indicators including latency, possible and actual throughput, power usage, and predicting achievement were measured in real pilot tests implemented in dense networks and IoT contexts. Available data were compared with other similar studies to gain an understanding of the results. Results: The conjoin with 5G and AI suggested potential optimization of process; the latency has been decreased to more than 90%, its predictive maintenance was sharpened, and its power consumption was decrease to 75%. The feasibility of extending scalability and system reliability of the protocol was confirmed in dense IoT environments, with further potential for emission reduction. Conclusion: The study identifies the use of 5G in Industry 4.0 with AI in addressing dynamic issues but potential drawback includes scalability and security. More studies should be conducted on the novel hybrid architectures and 6G integration concerning more extensive areas.

: 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.

Interpretation of dental panoramic radiographs which encompass all teeth as well as portions of the jaw and facial bones is critically important for preventive care and for devising appropriate treatment plans based on clinical findings. However, a high clinical workload or the absence of a specialist may compromise the accurate interpretation of even fundamental conditions, such as the detection of abnormalities. In such cases, artificial intelligence techniques can serve as valuable tools to enhance diagnostic accuracy. This research introduces a modified detection framework based on YOLOv11, incorporating two main architectural enhancements: the addition of a module designed to increase attention to specific regions, and improvements to the multi-scale blocks in the backbone of the network. The post-processing stage also employed methods capable of effectively distinguishing overlapping teeth. Experimental results demonstrate an improvement of over 7 percent in the F1-score compared to the baseline YOLOv11 architecture. The proposed model demonstrates competitive performance compared to models with similar architectures and exhibits satisfactory generalization on an independent dataset that was not utilized during training. Furthermore, relying on the real-time processing capability inherent to the YOLO framework, the proposed method can serve as an effective deep learning engine for integration into web software platforms and tools, enabling rapid and accurate dental radiograph analysis in clinical and telemedicine environments.

Over the past decades, the use of strategies by language learners has been the subject of considerable research. Yet comparatively few studies have examined the direct effects of strategy instruction on the actual writing strategy use of learners, with specific comparisons involving students at different levels of proficiency. This study examined if focused writing strategy instruction can increase the use of writing strategies by EFL learners. Using a mixed-methods design, 64 undergraduate learners in Iran were divided into experimental and control groups. The experimental group received strategy-based instruction grounded in Oxford’s (1990) framework, while the control group received conventional writing instruction. Data were collected through pre- and post-tests, stimulated recall protocols, and a validated strategy-use questionnaire. Findings from a mixed between-within ANOVA revealed significant time-group- proficiency interactions, indicating that strategy instruction benefited learners differently across proficiency levels. Qualitative analysis further highlighted how learners integrated strategies into their writing practices. The results have implications for tailoring writing instruction to proficiency-specific needs.

This study investigated the impact of newly developed English for Specific Purposes (ESP) materials on enhancing Intercultural Communicative Competence (ICC) and cultural awareness among Iranian undergraduate psychology students. Addressing the need for culturally responsive communication and limitations of conventional materials, researchers designed a 5-unit coursebook based on Byram’s (1997) ICC framework and the ICC materials development framework proposed by Mishan and Kiss (2024). Employing a mixed-methods design over a 10-week semester, 60 homogeneous students were equally assigned into two groups: an experimental group received newly developed ESP materials that integrated cross-cultural case studies, dialogic activities, and culturally diverse psychological modules, while the control group used traditional textbooks. Quantitative data from ICC questionnaires revealed a significant increase in ICC within the experimental group. Qualitative analysis of student reflections and interviews indicated enhanced intercultural attitudes, knowledge, skills, and cultural awareness, reflecting meaningful shifts in perspectives for the experimental group. These findings underscore the effectiveness of ICC-focused ESP materials in preparing psychology students for culturally sensitive practice. The study contributes to the development of ESP materials by advocating for the intentional integration of intercultural competence to cultivate globally competent practitioners.

Listening effectively in a foreign language presents many challenges for students. The aims of this convergent mixed methods study were two-fold. First, to determine whether teaching listening strategies through materials tailored to apply principles related to decreasing Cognitive Load (CL) could help learners improve their L2 listening comprehension. Second, whether using such materials could reduce learners' cognitive load. In doing so, two modes of material presentation (computer-based and text-based) were selected. Eighty-eight TEFL bachelor students in three intact classes were randomly assigned to three treatment conditions: Strategy-Based Cognitive-Load-Decreasing Computer-Based Material (SB-CLD-CBM), Strategy-Based Cognitive-Load-Decreasing Text-Based Material (SB-CLD-TBM), and Strategy-Based Non-Cognitive-Load Decreasing Conventional Learning Material (SB-NCLD-CLM), and received relevant instructions. A general proficiency test was used to examine participants' language proficiency. A listening pretest and posttest, a listening achievement test, and a cognitive load questionnaire were used to collect data in the quantitative phase. A reflection paper, including two questions about students' perceptions administered during the treatment, was used to gather qualitative data. The One-way Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA) showed that CLD-CBM and CLD-TBM outperformed the NCLD-CLM in listening comprehension and experienced lower cognitive load. However, no significant difference was found between the experimental groups. The findings are valuable for teachers and materials developers, suggesting they should include Cognitive Load Theory principles and strategy instruction when designing learning materials and instructional approaches.

The uncontrolled growth of tall buildings in contemporary cities has caused numerous visual and psychological issues for urban citizens. This study's framework examined the combined effect of architectural and vegetation variations on the scape of tall buildings and the oppression imposed on citizens. The architectural changes of the tall building included form (rectangular, stepped and pyramidal), gap (in single building (without gap, small and large gap) and between buildings (middle, sides and middle and sides)), console (1, 2, 2,5, 3, 3.5 and 4 m), and setback (1, 2 and 3 m) of the building from the base. Vegetation changes also included the distribution of different tree species on the façade and the positioning of the trees within each species. To assess the impact of architectural and vegetation changes on oppression, the method was based on the framework for assessing the oppressiveness of tall buildings and people's opinions. In conclusion, vegetation demonstrated a significantly greater reduction in perceived oppression compared to architectural modifications. The results of examining the architectural scenarios showed that as the pyramidal form of the building increased and the gap between the single tall building (or between tall buildings) widened, citizen-imposed oppression decreased. Also, as the console of a tall building decreased to less than 2.5 m and the setback increased to more than 2 m, the oppression decreased significantly. Also, the results showed that using concentrated and horizontal vegetation at the base and shaft of tall buildings has effectively reduced oppression (Compared to other vegetation change scenarios).