Accurate parameter estimation is crucial and challenging for the design and modeling of PV cells/modules. However, the high degree of non-linearity of the typical I–V characteristic further complicates this task. Consequently, significant research interest has been generated in recent years. Currently, this trend has been marked by a noteworthy acceleration, mainly due to the rise of swarm intelligence and the rapid progress of computer technology. This paper proposes a developed Mountain Gazelle Optimizer (MGO) to generate the best values of the unknown parameters of PV generation units. The MGO mimics the social life and hierarchy of mountain gazelles in the wild. The MGO was compared with well-recognized recent algorithms, which were the Grey Wolf Optimizer (GWO), the Squirrel Search Algorithm (SSA), the Differential Evolution (DE) algorithm, the Bat–Artificial Bee Colony Optimizer (BABCO), the Bat Algorithm (BA), Multiswarm Spiral Leader Particle Swarm Optimization (M-SLPSO), the Guaranteed Convergence Particle Swarm Optimization algorithm (GCPSO), Triple-Phase Teaching–Learning-Based Optimization (TPTLBO), the Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO), the quasi-Opposition-Based Learning Whale Optimization Algorithm (OBLWOA), and the Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO). The experimental findings and statistical studies proved that the MGO outperformed the competing techniques in identifying the parameters of the Single-Diode Model (SDM) and the Double-Diode Model (DDM) PV models of Photowatt-PWP201 (polycrystalline) and STM6-40/36 (monocrystalline). The RMSEs of the MGO on the SDM and the DDM of Photowatt-PWP201 and STM6-40/36 were 2.042717 $\times {10}^{-3}$, 1.387641 $\times {10}^{-3}$, 1.719946 $\times {10}^{-3}$, and 1.686104 $\times {10}^{-3}$, respectively. Overall, the identified results highlighted that the MGO-based approach featured a fast processing time and steady convergence while retaining a high level of accuracy in the achieved solution.

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.

An intense level of academic pressure causes students to experience stress, and if this stress is not addressed, it can cause adverse mental and physical effects. Since the pandemic situation, students have received more assignments and other tasks due to the shift of classes to an online mode. Students may not realize that they are stressed, but it may be evident from other factors, including sleep deprivation and changes in eating habits. In this context, this paper presents a novel ensemble learning approach that proposes an architecture for stress level classification. It analyzes certain factors such as the sleep hours, productive time periods, screen time, weekly assignments and their submission statuses, and the studying methodology that contribute to stress among the students by collecting a survey from the student community. The survey data are preprocessed to categorize stress levels into three categories: highly stressed, manageable stress, and no stress. For the analysis of the minority class, oversampling methodology is used to remove the imbalance in the dataset, and decision tree, random forest classifier, AdaBoost, gradient boost, and ensemble learning algorithms with various combinations are implemented. To assess the model’s performance, different metrics were used, such as the confusion matrix, accuracy, precision, recall, and F1 score. The results showed that the efficient ensemble learning academic stress classifier gave an accuracy of 93.48% and an F1 score of 93.14%. Fivefold cross-validation was also performed, and an accuracy of 93.45% was achieved. The receiver operating characteristic curve (ROC) value gave an accuracy of 98% for the no-stress category, while providing a 91% true positive rate for manageable and high-stress classes. The proposed ensemble learning with fivefold cross-validation outperformed various state-of-the-art algorithms to predict the stress level accurately. By using these results, students can identify areas for improvement, thereby reducing their stress levels and altering their academic lifestyles, thereby making our stress prediction approach more effective.

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.

Powered by deep learning (DL), autoencoders (AE) end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in traditional communication systems and have achieved great success. In this paper, a new probabilistic model, based on the variational autoencoders (VAE), is proposed for short packet wireless communication systems. Using this new approach, the information messages are represented by the so called packet hot vectors (PHV) which are inferred by the VAE latent random variables (LRVs). Then only LRVs’ parameters can be transmitted through the physical wireless channel. This results in a significant improvement in spectral efficiency when compared with the pure AE approach where a longer hot vectors are to be transmitted. Specific VAE models have been developed for both binary (BPSK) as well as Quadrature phase shift keying (QPSK) systems. Simulation and numerical results are given to demonstrate the performance of the proposed method in different real scenarios, including Rayleigh and Rician fading channels with Shadowing and Doppler effects. Our simulation and numerical results show that the new proposed VAE with a DL classifier can provide an improved symbol error rate (SER) performance than both the baseline AE and the classical Hamming code with hard decision decoding. Furthermore, as far as the spectral efficiency of the proposed method is concerned, we show that using two channels in the proposed VAE performance exceeds the 7 channels baseline AE.

Unmanned aerial vehicles (UAVs) offer advantages in serving as aerial small cells (ASCs) to support public safety terrestrial cells (PSTCs) while providing pervasive coverage during disasters. To ensure reliable communications for long-term evolution-based public safety (PS-LTE) users, it is crucial to obtain an accurate understanding of network performance for practical cell association design and network stability. This comprehension is vital for the practical design of cell associations and for maintaining network stability in next-generation consumer wireless networks. For this purpose, we first employ a flexible biased cell association (FBCA) policy that optimally selects the bias factor where a PS-LTE user (PUE) connects to the eNodeB (eNB) giving the maximum power for the received signal. Then, we present a resource allocation and subframe–type selection by formulating stochastic optimization programming to resolve system stability issues in the coexisting PS-LTE and LTE-based high-speed railway (LTE-R) networks and PS-LTE and UAV networks. In addition to this, we employ the Lyapunov optimization technique to seek an optimal almost blank subframe (ABS) algorithm with dynamic delay-aware resource allocation (ADDRA) to resolve the problem of network stability. Using ADDRA, the PS-LTE eNodeB (PSeNB), the aerial eNodeBs (AeNBs), and the LTE-R eNodeBs (ReNBs) obtain up-to-date queues of attached users and accordingly compute a matrix for scheduling resources based on channel state information (CSI) feedback. The simulation results of the UAV-assisted networks using FBCA and ADDRA in coexisting PS-LTE/LTE-R and PS-LTE/UAV networks demonstrate a significant improvement when compared with other state-of-the-art techniques.