This paper investigates the performance of vacuum gate dielectric doping-free carbon nanotube/nanoribbon field-effect transistors (VGD-DL CNT/GNRFETs) via computational analysis employing a quantum simulation approach. The methodology integrates the self-consistent solution of the Poisson solver with the mode space non-equilibrium Green’s function (NEGF) in the ballistic limit. Adopting the vacuum gate dielectric (VGD) paradigm ensures radiation-hardened functionality while avoiding radiation-induced trapped charge mechanisms, while the doping-free paradigm facilitates fabrication flexibility by avoiding the realization of a sharp doping gradient in the nanoscale regime. Electrostatic doping of the nanodevices is achieved via source and drain doping gates. The simulations encompass MOSFET and tunnel FET (TFET) modes. The numerical investigation comprehensively examines potential distribution, transfer characteristics, subthreshold swing, leakage current, on-state current, current ratio, and scaling capability. Results demonstrate the robustness of vacuum nanodevices for high-performance, radiation-hardened switching applications. Furthermore, a proposal for extrinsic enhancement via doping gate voltage adjustment to optimize band diagrams and improve switching performance at ultra-scaled regimes is successfully presented. These findings underscore the potential of vacuum gate dielectric carbon-based nanotransistors for ultrascaled, high-performance, energy-efficient, and radiation-immune nanoelectronics.

Conventional methodologies such as Incremental Conductance (IC) and Perturbation and Observation (P&O) can be considered effective and low-cost solutions for PV Maximum Power Point Tracking (MPPT) problems in most cases. However, these methods fail to guarantee global maximum tracking in certain situations, such as multiple peak challenges caused by Partial Shading Conditions (PSCs). Therefore, metaheuristic algorithms, like Particle Swarm Optimization (PSO), are employed in literature to address the MPPT problems during PSCs. Nevertheless, traditional PSO encounters issues such as slow convergence and a high probability of failure in tracking the global maximum power point during complex PSCs, which cause a reduction of system efficiency. To address these issues, a modified PSO hybrid with a finite control set Model Predictive Control (MPSO-MPC) has been developed as a robust MPPT technique. The MPC is incorporated into the proposed method to increase the tracking speed. The proposed approach combines a new initialization scheme that ensures uniform initial population distribution across the P-I curve. Additionally, an innovative method is used to update the search space once the partial shading pattern is detected to include only the feasible solutions in the search process. Finally, Incremental Conductance (IC) is introduced to refine the tracking process of global peak and increase its efficiency. The proposed MPSO-MPC algorithm is implemented using dSPACE MicroLabBox for real-time applications. Comprehensive investigations through MATLAB/Simulink simulation and experimental studies validate that the developed method outperforms traditional PSO and Cuckoo Search (CS) algorithms, with a convergence time that does not exceed 0.35 s and a tracking efficiency above 99.5 % under various complex PSCs. Furthermore, the results demonstrate that the proposed technique outperforms both PSO and CS across a range of environmental conditions and load disturbances.

The presented research paper proposes a novel integrated technique combining LeNet-5 with Continuous Wavelet Transform (CWT) along with Long Short-Term Memory (LSTM). The purpose of this integration is to improve the performance of mechanisms used for the detection of defects in rotatory machines across various operating conditions. The Convolutional Neural Networks (CNN) assists the presented CWT-LeNet-5-LSTM technique in finding the complex characteristics in the data, while LSTM learns the trends in the dataset and performs the necessary analysis of vibrations occurring in faulty machines. The developed model was examined for various loads and faults to extract results having accuracies of 99.6%, 96.9%, 92.5% and 96.6% for load conditions 3, 2, 1, and 0, respectively. These results demonstrate the ability of the proposed model to adapt according to varying load conditions while having the necessary levels of accuracy. This validates the model to perform precise fault detection and diagnosis, offering capabilities of predictive maintenance in industrial settings.

This research paper focuses on the application of a new method for the simultaneous reconfiguration and the optimum placing of Soft Open Points (SOPs) in Radial Distribution Systems (RDS). The proposed Lévy Flight-based Improved Equilibrium Optimizer (LF-IEO) algorithm enhances the standard Equilibrium Optimizer (EO) by integrating several techniques to improve exploration and exploitation capabilities. SOPs are highly developed power electronics devices that can enhance distribution utility networks in terms of reliability and effectiveness. However, identifying their optimum place along with network reconfiguration is a challenging task that requires advanced computation techniques. The performance of the proposed LF-IEO algorithm has been first verified on several benchmark functions. Subsequently, it is implemented on a IEEE 33-Bus, 69-Bus, 118-Bus, and Algerian 116-Bus distribution network to solve the problem of simultaneous network reconfiguration and optimal SOP placement. For the Algerian 116-bus system case study, the algorithm achieved a significant 14.89% reduction in power losses, improved the minimum voltage, and generated substantial net annual savings of 74,426.40 $/year. To prove its superiority in terms of solution quality and robustness, the proposed LF-IEO approach was compared with several newly developed algorithms from the literature.