Energy-efficient RL-based aerial network deployment testbed for disaster areas

Rapid deployment of wireless devices with 5G and beyond enabled a connected world. However, an immediate demand increase right after a disaster paralyzes network infrastructure temporarily. The continuous flow of information is crucial during disaster times to coordinate rescue operations and identify the survivors. Communication infrastructures built for users of disaster areas should satisfy rapid deployment, increased coverage, and availability. Unmanned air vehicles (UAV) provide a potential solution for rapid deployment as they are not affected by traffic jams and physical road damage during a disaster. In addition, ad-hoc WiFi communication allows the generation of broadcast domains within a clear channel which eases one-to-many communications. Moreover, using reinforcement learning (RL) helps reduce the computational cost and increases the accuracy of the NP-hard problem of aerial network deployment. To this end, a novel flying WiFi ad-hoc network management model is proposed in this paper. The model utilizes deep-Q-learning to maintain quality-of-service (QoS), increase user equipment (UE) coverage, and optimize power efficiency. Furthermore, a testbed is deployed on Istanbul Technical University (ITU) campus to train the developed model. Training results of the model using testbed accumulates over 90% packet delivery ratio as QoS, over 97% coverage for the users in flow tables, and 0.28 KJ/Bit average power consumption

TÜRKİYE'DE VERGİ UYUMUNU ETKİLEYEN DEĞİŞKENLER

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Analysis of the YouTube videos on pelvic floor muscle exercise training in terms of their reliability and quality

Purpose The aim of this study was to evaluate the content, reliability and quality of YouTube videos related to pelvic floor muscle exercise training. Method This study was carried out on the descriptive model in order to evaluate the content, reliability and quality of the videos on YouTube related to pelvic floor muscle exercise training. "Pelvic floor muscle exercise" was searched on YouTube in English in March 2020, and a total of 107 videos were watched. Quality Criteria for Consumer Health Information (DISCERN) survey was used to analyze the videos in terms of their reliabilities, and Global Quality Score (GQS) was used to evaluate their qualities. Results When the contents of 59 videos included in the study were examined, it was determined that 52 of them contained useful information and 7 of them contained misleading information. Comprehensiveness mean scores, DISCERN mean scores and GQS means of the useful videos were found to be statistically higher than that of the moderate and misleading videos (p < 0.05).When videos were analyzed according to the publishing sources, 84.62% (44/52) of the useful videos and 85.71% (6/7) of misleading video were observed to be published by independent health information websites. No statistically significant difference was found between the overall comprehensiveness mean scores, DISCERN mean scores, GQS means and the features of the videos according to their publishing sources. Conclusion In this study, it was observed that the vast majority of YouTube videos on pelvic floor muscle exercise training were useful videos; the vast majority of these videos were published by independent health information websites and contained moderately safe, accurate and quality information