Sura Sabah

Background: IoT Smart networks are the latest creation of smart technology where Internet of Things, Artificial Intelligence, and Blockchain technologies have merged. Such technologies have the possibility of increasing performance, security and the degree of expansion in different fields like smart city, health and manufacturing. As it is, there are several issues that organisations continued to encounter when implementing both these systems in order to address diversified network requirements. Objective: The study aims to define how IoT, AI, and Blockchain technologies can be integrated to develop smart networks and how their integration will address the issues of performance, data integrity, and resource utilization in smart networks. Methods: The solution consisted of three components: IoT for instant data gathering, AI for modeling and efficient traffic control, Blockchain for secure data storage. Analyses of various objectives such as data throughput, latency, energy consumption, and security were conducted for smart city applications through simulations. Results: The linked matrix obtained a 45% increase in data transfer rate, a 40% cut in response time and a 50% enhancement of power utilization compared to other systems. Purchases made using blockchain were correct to the last digit, achieved with a success rate of 99.9%, and there were no cases of hacking. AI algorithms minimized congestion levels of the network by 55%, and IoT devices remained available 98% of the time. Conclusion: The incorporation of the IoT, AI and Blockchain enhances the effectiveness and assures the stability of smart networks greatly. From these findings, there is a significant potential for broad utility thus the need for research on the scale, integration, and testing of these in practice.

Background: Recent advancements in Natural Language Processing (NLP) have been significantly influenced by transformer models. However, challenges related to scalability, discrepancies between pretraining and finetuning, and suboptimal performance on tasks with diverse and limited data remain. The integration of Reinforcement Learning (RL) with transformers has emerged as a promising approach to address these limitations. Objective: This article aims to evaluate the performance of a transformer-based NLP model integrated with RL across multiple tasks, including translation, sentiment analysis, and text summarization. Additionally, the study seeks to assess the model's efficiency in real-time operations and its fairness. Methods: The hybrid model's effectiveness was evaluated using task-oriented metrics such as BLEU, F1, and ROUGE scores across various task difficulties, dataset sizes, and demographic samples. Fairness was measured based on demographic parity and equalized odds. Scalability and real-time performance were assessed using accuracy and latency metrics. Results: The hybrid model consistently outperformed the baseline transformer across all evaluated tasks, demonstrating higher accuracy, lower error rates, and improved fairness. It also exhibited robust scalability and significant reductions in latency, enhancing its suitability for real-time applications. Conclusion: This article illustrates that the proposed hybrid model effectively addresses issues related to scale, diversity, and fairness in NLP. Its flexibility and efficacy make it a valuable tool for a wide range of linguistic and practical applications. Future research should focus on improving time complexity and exploring the use of deep unsupervised learning for low-resource languages.

Background: As the demand for ultra-fast, low-latency communication continues to rise, Terahertz (THz) communication has emerged as a promising candidate for enabling next-generation 6G networks. However, environmental sensitivity and hardware challenges pose significant limitations. Objective: This study investigates the potential of THz communication to support ultra-high data transfer rates in 6G networks, with a focus on the impact of environmental conditions, hardware complexity, and modulation techniques. Method: Through simulation analysis under both optimal and adverse environmental conditions, the performance of THz communication was assessed. The study also explores emerging materials and adaptive technologies to mitigate performance degradation. Results: Under optimal conditions, THz communication demonstrated the ability to achieve data rates up to 8.5 Tbps with approximately 1 ms latency at 10 THz. However, in high humidity and non-line-of-sight (NLOS) scenarios, performance declined significantly, with the signal-to-noise ratio (SNR) dropping from 35 dB to 18 dB and the bit error rate (BER) increasing from 3×10⁻³ to 4×10⁻². Orthogonal Frequency Division Multiplexing (OFDM) outperformed Quadrature Amplitude Modulation (QAM) in BER under varying conditions. The integration of advanced materials such as graphene and photonic crystals, along with intelligent reflecting surfaces (IRS), showed promise in enhancing signal quality and thermal management. Conclusion: While THz communication exhibits strong potential for supporting the high-speed, low-latency demands of 6G, environmental vulnerabilities and hardware complexity remain key challenges. Future research should prioritize the development of cost-effective, scalable materials and adaptive technologies to improve performance and deployment feasibility in diverse conditions.