Multilevel inverters (MLIs) have become fundamental in contemporary power electronics, providing enhanced performance compared to conventional two-level inverters regarding their output voltage quality, efficiency, and scalability. This study comprehensively assesses multilevel inverter technologies, including their topologies, control systems, and various applications. The study starts with a comprehensive examination of the core concepts of MLIs, subsequently embarking on a detailed evaluation of both conventional and innovative topologies, such as diode-clamped, flying capacitor, cascaded H-bridge, and modular multilevel converters. The study further examines the control systems used in MLIs, including Pulse Width Modulation (PWM), space vector modulation (SVM), and Model Predictive Control (MPC), emphasizing their benefits and drawbacks. The applications of MLIs in renewable energy systems, electric cars, industrial drives, and grid integration are comprehensively examined. The study closes by examining growing trends, difficulties, and future research paths, emphasizing the ability of MLIs to transform power conversion systems.

Thermal energy storage (TES) plays a vital role in advancing energy efficiency and sustainability, with phase change materials (PCMs) receiving significant attention due to their high latent heat storage capacity. Nevertheless, conventional PCMs face critical challenges such as leakage, phase separation, and low thermal conductivity, which hinder large-scale applications. Encapsulation strategies have been developed to address these issues, and bio-based composite materials are increasingly recognised as sustainable alternatives. Materials such as lignin, nanocellulose, and biochar, as well as hybrid formulations with graphene and aerogels, show promise in improving thermal conductivity, mechanical integrity, and environmental performance. This review evaluates bio-based encapsulation approaches for PCMs, examining their effectiveness in enhancing heat transfer, durability under thermal cycling, and scalability. Applications in solar energy systems, building insulation, and electronic thermal regulation are highlighted, as are emerging AI-driven modelling tools for optimising encapsulation performance. Although bio-based PCM composites outperform conventional systems in terms of thermal stability and multifunctionality, they still face persistent challenges in terms of cost-effectiveness, scalability, and long-term reliability. Future research on smart, multifunctional PCMs and advanced bio-nanocomposites is essential for realising next-generation TES solutions that combine sustainability, efficiency, and durability.

Electric vehicle (EV) customers are expected to charge EV batteries at a rapid EV charging station or via on-road wireless EV charging systems when possible, as per their charging needs to successfully complete any remaining trips and reach their destination. When on-road wireless EV charging systems are considered as an alternative charging method for EVs, this can affect the load of a rapid EV charging station in terms of time and magnitude. Hence, this paper presents a probabilistic framework for estimating the arrival rate of EVs at an EV rapid charging station, considering the availability of on-road wireless charging systems as an alternative charging method. The proposed model incorporates an Electric Vehicle Decision Tree that predicts the times when EVs require rapid charging based on realistic transportation data. A Monte Carlo simulation approach is used to capture uncertainties in EV user decisions regarding charging types. A queuing model is then developed to estimate the charging load for multiple EVs at the charging station, with and without the consideration of on-road EV wireless charging systems. A case study and simulation results considering a 32-bus distribution system and the US National Household Travel Survey (NHTS) data are presented and discussed to demonstrate the impact of on-road wireless EV charging on the loads of an rapid EV charging station. It is observed that having on-road wireless EV charging as complementary charging to EV charging stations helps to significantly reduce the peak load of the charging station, which improves the power system capacity and defers the need for system upgrades.

Electric vehicle (EV) users have the flexibility to fulfill their charging needs using either high-speed charging stations or innovative on-road wireless charging systems, ensuring uninterrupted travel to their destinations. These options present a spectrum of benefits, enhancing convenience and efficiency. The adoption of on-road wireless charging as a complementary method influences both the timing and extent of demand at fast-charging stations. This study introduces a comprehensive probabilistic framework to analyze EV arrival rates at fast-charging facilities, incorporating the impact of on-road wireless charging availability. The proposed model utilizes transportation data, including patterns from the US National Household Travel Survey (NHTS), to predict the specific times when EVs would need fast charging. To account for uncertainties in EV user decisions concerning charging preferences, a Monte Carlo simulation (MCS) approach is employed, ensuring a comprehensive analysis of charging behaviors and their potential impact on charging stations. A queuing model is developed to estimate the charging demand for numerous electric vehicles at a charging station, considering both scenarios: on-road EV wireless charging and relying exclusively on fast-charging stations. This study includes an analysis of a case and its simulation results based on a 32-bus distribution system and data from the US National Household Travel Survey (NHTS). The results indicate that integrating on-road EV wireless charging as complementary to fast charging significantly reduces the peak load at the charging station. Additionally, considering the on-road EV wireless charging system, the peak load of the station no longer aligns with the peak load of the power grid, resulting in improved power system capacity and deferred system upgrades.

This research provides a detailed quantitative analysis of the challenges encountered in deploying 6G Unmanned Aerial Vehicle Base Stations UAV-BS in arid environments, specifically focusing on Saudi Arabia. The study meticulously quantifies the impact of dust and sand storms on these advanced communication systems, highlighting the substantial increase in signal attenuation and path loss under such conditions. Key findings include a marked dependency of transmitted power requirements on visibility at millimeter range high frequencies, (at 100 GHz). The results demonstrate that at this frequency, a decrease in ground visibility from 15 to 5 meters necessitates a significant increase in transmitted power by approximately 10 dB. Moreover, a striking 50 dB increase in path loss is observed when transitioning from a 3 GHz carrier frequency to 100 GHz under a ground visibility of 5 meters. This increase is attributed to a 30 dB rise in free space loss FSL due to the change in carrier frequency and an additional 20 dB attributed to increased dust attenuation at 100 GHz compared to 3 GHz. Our approach integrates Particle Swarm Optimization (PSO) for dynamic UAV-BS positioning, effectively mitigating the adverse effects of dust and sand on signal quality. The research underscores the importance of considering UAV altitude, signal frequency, and ground visibility for optimal signal propagation. Additionally, it highlights the necessity of dynamic power management strategies within the network architecture to adapt to the unique challenges of arid environments. This study not only illuminates the practical implications for the deployment of UAV-BS in future 6G networks but also emphasizes the need for continuous adaptation of UAV-BS positioning in response to changes in visibility and that due to continuous change in the pattern of active users (asking for network services). The insights gained are crucial for ensuring reliable communication during environmental challenges, paving the way for more resilient and efficient wireless communication systems in demanding conditions.

Accurate and real-time measurement of fluid flow velocity is crucial in various industrial processes, especially when dealing with multiple phase fluids. Traditional flow measurement methods often struggle to accurately quantify the velocity of complex multiphase flows within pipes. This challenge necessitates the exploration of innovative techniques capable of providing reliable measurements. This paper proposes the utilization of Electrical Capacitance Tomography (ECT) as a promising approach for measuring the velocity of multiple phase fluids in pipes. The ECT technique involves the non-intrusive imaging of the electrical capacitance distribution within the pipe. By utilizing an array of electrodes placed around the pipe circumference, the capacitance distribution can be reconstructed, offering insight into the fluid flow patterns. By analyzing the temporal changes in the capacitance distribution, the velocity of different phases within the pipe can be estimated. To achieve accurate velocity measurements, an ECT system needs to account for the complexities introduced by multiphase flows. Various image reconstruction algorithms, such as linear back-projection and iterative algorithms like Gauss-Newton and Levenberg-Marquardt, are employed to reconstruct the capacitance distribution. Additionally, advanced signal processing techniques, such as cross-correlation analysis and time-difference methods, are used to extract velocity information from the reconstructed images. This paper presents an experimental investigation of measuring the velocity of multiple-phase fluids in pipes using the ECT technique. The study aims to address the challenges associated with different flow regimes, fluid properties, and pipe geometries by exploring advancements in electrode design, system calibration, and data processing techniques to enhance the accuracy and robustness of ECT-based velocity measurements.