دور وسائل الإعلام المدرسي في تنمية المسؤولية المجتمعية لدى طلبة المدارس الثانوية في قصبة إربد الأُردن من وجهة نظر المدرسين

هدف المقال التعرف إلى دور وسائل الإعلام المدرسي باعتبارها من أهم الوسائل التربوية في تنمية المسؤولية المجتمعية - من خلال ما تقدمه من مواد علمية وثقافية - لدى الطلبة من وجهة نظر أفراد عينة الدراسة البالغ عددهم 50 معلماً. استخدم الباحثان المنهج الوصفي التحليلي، والاستبانةالتي تكونت من (26) فقرة، بعد التأكد من صدقها وثباتها، كأداة لجمع بيانات الدراسة. واستُخدم برنامج (SPSS) لتحليل بياناتها واستخلاص نتائجها. أهم النتائج: أن هناك دوراً ايجابياً مرتفعاً المستوى لوسائل الإعلام المدرسية في تنمية المسؤولية المجتمعية لدى الطلبة، حيث كان تأثير هذا الدور مرتفع في تنمية المسؤولية تجاه المدرسة وبيئتها، وبدرجة متوسطة في تنمية المسؤولية تجاه المجتمع المحلي. وجود فروق دالة احصائياً عند مستوى الدلالة (0.05≥α) بين متوسطات استجابات أفراد العينة تبعًا للجنس ولصالح الذكور. وعدم وجود فروق تبعاً لباقي متغيرات الدراسة. أهم التوصيات: تفعيل دور وسائل الإعلام المدرسية وخاصة الإذاعة المدرسية لبث الوعي الوطني والتعريف بالمسؤولية المجتمعية التي تعتبر من أهم قيم المواطنة الصالحة لدى الناشئة من الطلبة. التفعيل الايجابي للاستخدام الصحيح للهواتف النقالة كونها من وسائل التواصل الاجتماعي الفعّالة. The article aimed to identify the role of the school media as one of the most important educational methods in developing social responsibility - through what it provides of scientific and cultural materials - among students in Irbid Secondary Schools from a study sample members consisted of (50) individuals. The researchers used the descriptive approach and the questionnaire as a tool to collect data which consisted of (26) paragraphs، after verifying its validity and reliability. The (SPSS) program was used to analyze its data. The results: There is a high-level positive role for school media in developing social responsibility among students as towards the school and its environment، and with a moderate degree in developing responsibility towards the local community. The presence of statistically significant differences at (0.05≥α) between the responses according to gender and in favor to males. Lack of differences according to the rest of the study variables. The Recommendations: Activating the role of school media، especially school radio، to broadcast national awareness and define social responsibility which is considered one of the most important values of good citizenship among young students

Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition

This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track objects movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency

Intelligent beam blockage prediction for seamless connectivity in vision-aided next-generation wireless networks

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition, this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE)

FedraTrees: a novel computation-communication efficient federated learning framework investigated in smart grids

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing energy consumption information to perform centralised learning may compromise data privacy and make it vulnerable to misuse, in addition to incurring high transmission overhead on communication resources. This study addresses these issues by utilising federated learning (FL), an emerging technique that performs ML model training at the user/substation level, where data resides. We introduce FedraTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based FL stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedraTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources

Blockchain-assisted UAV communication systems: a comprehensive survey

Unmanned aerial vehicles (UAVs) have recently established their capacity to provide cost-effective and credible solutions for various real-world scenarios. UAVs provide an immense variety of services due to their autonomy, mobility, adaptability, and communications interoperability. Despite the expansive use of UAVs to support ground communications, data exchanges in those networks are susceptible to security threats because most communication is through radio or Wi-Fi signals, which are easy to hack. While several techniques exist to protect against cyberattacks. Recently emerging technology blockchain could be one of promising ways to enhance data security and user privacy in peer-to-peer UAV networks. Borrowing the superiorities of blockchain, multiple entities can communicate securely, decentralized, and equitably. This article comprehensively overviews privacy and security integration in blockchain-assisted UAV communication. For this goal, we present a set of fundamental analyses and critical requirements that can help build privacy and security models for blockchain and help manage and support decentralized data storage systems. The UAV communication system's security requirements and objectives, including availability, authentication, authorization, confidentiality, integrity, privacy, and non-repudiation, are thoroughly examined to provide a deeper insight. We wrap up with a discussion of open research challenges, the constraints of current UAV standards, and potential future research directions

Federated Learning for Reliable mmWave Systems: Vision-Aided Dynamic Blockages Prediction

No abstract available

A Hybrid Data Manipulation Approach for Energy and Latency-Efficient Vision-Aided UDNs

The combination of deep learning (DL) and computer vision (CV) is shaping the future of wireless communications by supporting the operations of ultra-dense networks (UDNs). However, vision-aided wireless communications (VAWC) are highly dependent on DL algorithms that rely on a wide range of multimodal data stored at a central location. Although the performance of the DL model is improved when the model becomes deeper, the need for a large number of datasets for model training incurs more computational complexity in terms of model training time and storage size. Hence, the energy efficiency of the network will become worse due to the higher energy costs associated with model training and transmitting a large amount of data over wireless links. Therefore, a crit-ical challenge is to reduce the computational complexity and bandwidth utilisation of DL-based vision-aided UDNs without compromising their performance. In this paper, we adopt single-channel (SICH) images, joint photographic expert group (JPEG) image compression (COMP), and object detection (ODET) to form a hybrid data manipulation technique. This technique can reduce the model computation cost and data storage volume, as well as alleviate the transmission burden on the wireless links to make future wireless networks more reliable and energy efficient. Specifically, this technique is used to manipulate datasets before using them in model training. Compared to reference datasets, simulation results show that our hybrid technique achieves the best performance in reducing the model computation by 34%, a significant reduction of 86% in memory size for data storage, reducing data transmission time by 83%, and 82.5% more energy efficient networks

BETA-UAV: Blockchain-based Efficient and Trusted Authentication for UAV Communication

No abstract available

Energy-Saving Load Control of Induction Electric Motors for Drives of Working Machines to Reduce Thermal Wear

The influence of reduced voltage on the service life of an induction motor is considered in this article. An algorithm for calculating the rate of thermal wear of induction motor insulation under reduced supply voltage depending on the load and the mechanical characteristics of the working machine has been developed. It determines the change in the rate of thermal wear under alternating external effects on the motor (supply voltage and load) and allows forecasting its service life under these conditions. The dependency graphs of the rate of insulation thermal wear on the motor load for various levels of supply voltage and various mechanical characteristics of working machines are provided in the work. It was determined that the rate of thermal wear of the induction motor insulation increases significantly when the voltage is reduced compared to its nominal value with nominal load on the motor. The authors propose to consider this fact for resource-saving control of the motor. The paper presents the results of experimental verification of the obtained rule for “Asynchronous Interelectro” (AI) series electric motors that confirm its accuracy. Based on the obtained correlation, the rule of voltage regulation in energy-saving operation mode has been derived. The proposed rule takes into account the thermal impact on the electric motor running in energy-saving mode and enables saving its resource, which, in turn, results in extending its service life. The research does not consider additional effects on the electric motor except the thermal one