AI Ultra

Nura A. Alhaj, M. F. Jamlos, S. Manap, Samah Abdelsalam, Abdelmoneim A. Bakhit, R. Mamat, M. A. Jamlos, M. Gismalla, Mosab Hamdan

The fifth-generation (5G) wireless communications have been deployed in many countries with the following features: wireless networks at 20 Gbps as peak data rate, a latency of 1-ms, reliability of 99.999%, maximum mobility of 500 km/h, a bandwidth of 1-GHz, and a capacity of 106 up to Mbps/m2. Nonetheless, the rapid growth of applications, such as extended/virtual reality (XR/VR), online gaming, telemedicine, cloud computing, smart cities, the Internet of Everything (IoE), and others, demand lower latency, higher data rates, ubiquitous coverage, and better reliability. These higher requirements are the main problems that have challenged 5G while concurrently encouraging researchers and practitioners to introduce viable solutions. In this review paper, the sixth-generation (6G) technology could solve the 5G limitations, achieve higher requirements, and support future applications. The integration of multiple access techniques, terahertz (THz), visible light communications (VLC), ultra-massive multiple-input multiple-output ( $\mu {\mathrm{ m}}$ -MIMO), hybrid networks, cell-free massive MIMO, and artificial intelligence (AI)/machine learning (ML) have been proposed for 6G. The main contributions of this paper are a comprehensive review of the 6G vision, KPIs (key performance indicators), and advanced potential technologies proposed with operation principles. Besides, this paper reviewed multiple access and modulation techniques, concentrating on Filter-Bank Multicarrier (FBMC) as a potential technology for 6G. This paper ends by discussing potential applications with challenges and lessons identified from prior studies to pave the path for future research.

Jacob Wekalao, Hussein A. Elsayed, Nassir Alarifi, Mostafa R. Abukhadra, Stefano Bellucci, A. Mehaney

Recent advancements in gas sensing technologies have significantly enhanced the detection and monitoring of gases across various applications, including environmental protection and industrial safety. This paper presents a novel metasurface-based sensor design that integrates advanced two-dimensional materials, such as graphene, copper, and MXene, to achieve high sensitivity and selectivity in terahertz gas detection. The proposed architecture features a central circular resonator surrounded by a square ring resonator, optimized for plasmonic modes, and an additional gold-coated circular ring to amplify detection capabilities. Through comprehensive modeling and simulation, the sensor’s performance was optimized, demonstrating remarkable sensitivity with a peak value of 800 GHz/RIU and robust responses across various gas concentrations. Moreover, the implementation of polynomial regression models further demonstrates the relationship between structural parameters and detection performance, achieving perfect predictive accuracy (R2 = 1.00). The results indicate that this innovative design not only addresses the growing demand for efficient gas sensing solutions but also sets the stage for future developments in sensor technology, with implications for healthcare diagnostics and environmental monitoring.

Chaima Chabira, Ibraheem Shayea, G. Nurzhaubayeva, L. Aldasheva, D. Yedilkhan, Saule Amanzholova

This paper presents a comprehensive review of handover management and load balancing optimization (LBO) in ultra-dense 5G and emerging 6G cellular networks. With the increasing deployment of small cells and the rapid growth of data traffic, these networks face significant challenges in ensuring seamless mobility and efficient resource allocation. Traditional handover and load balancing techniques, primarily designed for 4G systems, are no longer sufficient to address the complexity of heterogeneous network environments that incorporate millimeter-wave communication, Internet of Things (IoT) devices, and unmanned aerial vehicles (UAVs). The review focuses on how recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), are being applied to improve predictive handover decisions and enable real-time, adaptive load distribution. AI-driven solutions can significantly reduce handover failures, latency, and network congestion, while improving overall user experience and quality of service (QoS). This paper surveys state-of-the-art research on these techniques, categorizing them according to their application domains and evaluating their performance benefits and limitations. Furthermore, the paper discusses the integration of intelligent handover and load balancing methods in smart city scenarios, where ultra-dense networks must support diverse services with high reliability and low latency. Key research gaps are also identified, including the need for standardized datasets, energy-efficient AI models, and context-aware mobility strategies. Overall, this review aims to guide future research and development in designing robust, AI-assisted mobility and resource management frameworks for next-generation wireless systems.

Mohamed Abdellatief, Wafa Hamla, Hassan Hamouda

Ultra-high-performance fiber-reinforced concrete (UHPFRC) is an exceptional type of cementitious composite with superior mechanical and durability performances. Achieving these properties involves maintaining a low water-to-cement ratio, optimizing aggregate size distribution, and integrating fiber reinforcement. Recently, there has been a notable trend in the development and application of UHPFRCs. However, there is still a requirement for artificial intelligence (AI) methods to predict the early-age compressive strength (CS) of UHPFRC and to define the key input factors for optimal mix design with appropriate proportions. Therefore, five AI models were chosen to assess the predictive accuracy of early-age CS in the current study. These models include support vector regression (SVR), random forest (RF), artificial neural network (ANN), gradient boosting (GB), and Gaussian Process Regression (GPR). As part of evaluating model performance and conducting error analysis, this study investigated differences in prediction accuracy among five models across training and testing datasets. Additionally, feature importance analysis was implemented to explore the influence of the input variables on the early-age CS. Results indicate that GPR and SVR models with high predictive accuracy (R2 > 0.90) outperformed ANN, RF, and GB models. Water, superplasticizer, curing temperature, and fiber content emerged as the most significant controlling parameters affecting early-age CS. The analysis of the interaction among the significant input variables and early-age CS suggests recommended inclusion levels for optimal performance. Specifically, it is recommended that the water content be maintained between 145 and 155 kg/m2, the superplasticizer content between 30 and 40 kg/m2, and the fiber content exceed 200 kg/m2. These recommendations are aimed at achieving desirable early-age CS characteristics. The overall findings reveal that the AI models can effectively improve the monitoring of early-age CS of UHPFRC.

Yassmin Seid Ahmed, F. Amorim

Geometric inaccuracies in machine configuration and part specifications are a major source of errors in CNC machining. These discrepancies have long affected the quality of manufactured components and continue to be a key research area in academia and industry. Over the years, significant efforts have been made to minimize these errors and enhance machining precision. Researchers have explored various methodologies to identify, measure, and compensate for spatial inaccuracies, improving accuracy in modern machining systems. This paper comprehensively reviews recent advancements in geometric error measurement and compensation techniques, particularly in five-axis machine tools. It examines the latest methods for detecting errors and explores volumetric error modeling approaches designed to enhance machining precision. This review highlights the growing role of emerging technologies, including on-machine measurement systems, machine learning algorithms, and digital twin frameworks, in improving real-time error detection and compensation strategies. Furthermore, advanced tools such as laser interferometry and hybrid software–hardware approaches are discussed for their potential to drive innovation in ultra-precision machining. This paper also addresses key challenges in achieving high volumetric accuracy and outlines future opportunities for improving CNC machining performance. Future research can enhance precision and reliability in modern manufacturing by integrating intelligent systems and advanced measurement techniques.