In this paper, a split-gate junctionless transistor (SG-JLT) is modeled, and the simulation is carried out. The I–V characteristic of the SG-JLT was compared and analyzed with the symmetric double gate junctionless transistor (SDG JLT) under the same parameters using Cogenda Visual 3D TCAD. The result shows that SG-JLT has a high ON-state current. The device characteristics of SG-JLT, such as drain current variation with drain voltage under different oxide thicknesses and surface potential variations, were studied.
In this research article, a Vertical Junctionless Field Effect Transistor (V-JLFET) is designed for Hydrogen (H2) gas sensing. This device utilizes a palladium (Pd) electrode which is a catalytic metal for sensing of hydrogen gas. When the H2 gas meets catalytic Pd electrode, it creates pressure, causing a shift in the Pd gate workfunction. This change in workfunction is employed to detect the occurrence of H2 gas. Several electrical characteristics such as current through drain (IDS), transconductance (gm), drain conductance (gd), surface potential (Φs), and electron concentration were studied to sense the existence of H2 gas. Additionally, a sensitivity analysis is carried out to assess the effectiveness of the device in terms of threshold voltage (Vth) with varying pressure.
The primary focus in the era of aiming for a clean and green ecosystem is to limit the emissions released when the energy generation takes place through conventional method. One way to achieve this target is to develop a feasible alternative to fossil or gas-based conventional power plants by combining several distributed resources (DRs) or distributed energy resources (DERs) to form a network of virtual power plant (VPP). This paper explores the potential and avenues of a VPP, one of the latest technological advancements the energy industry has witnessed in the last few years. A VPP identifies the small-scale DERs and combines them, aggregated afterwards to have an energy hub that can cater to the need of power requirement. They expand the economic profits for the members, including micro DER owners, prosumers and system operators and also reduce emissions as the majority of sources compiled in the VPP network are renewables. To extract the maximum potential of VPP, optimal scheduling of the resources is essential to leverage the developed network’s efficacy. This can be achieved by employing highly advanced metaheuristic techniques to manage these flexible resources given their intermittencies and uncertain nature. The selected algorithm must be capable of handling a complex problem by reducing the computational burden and achieving the best optimum solution in line with the target objective function. The multiple benefits obtained from employing the VPP to perform effective energy management are highlighted in this work by involving various scheduling strategies and a possible direction of leveraging the potential of small-scale DERs that can contribute to grid-related services.
This work is intended to present a nested Photonic Crystal Fiber (PCF) for OAM mode propagation. The proposed PCF comprises of a dense flint SF6 and a very light flint LLF1. The finite-element method (FEM) is used for the modal analysis to obtain propagating HE/EH modes. The proposed fiber has shown flat dispersion during the propagation of multiple orbital angular momentum (OAM) modes. Furthermore, the designed PCF’s nonlinearity (gamma) and confinement loss (CL) are calculated to prove the practical feasibility of the design.
Various real-world applications, including as text categorization, categorization of gender in facial recognition for medical evaluation, fraud detection, and satellites analysis of images for oil-spill monitoring, are frequently plagued by imbalanced data. The majority class is commonly the primary focus of machine learning algorithms, with the minority samples being ignored or classified in a secondary manner. Nevertheless, despite their rarity, these minority samples are very important. When it comes to classification tasks, the issue of class imbalance—where one class is underrepresented relative to another—presents a significant barrier. Specialized approaches including SMOTE, ADASYN, and cost-sensitive voting classifiers have been developed to address this problem. The minority class is oversampled in these methods, synthetic samples are created adaptively, and different prices are placed on misclassification mistakes in order to solve the issue of class imbalance. As a result, rigorous assessment utilizing pertinent metrics and cost considerations are required. The efficacy of these strategies, however, depends on dataset features and problem-specific factors. Class imbalance is still a hot topic for study, and there has been constant innovation in novel methods that are adapted to certain dataset characteristics and application fields.
