RF performance evaluation of p-type NiO-pocket based β-Ga2O3/black phosphorous heterostructure MOSFET

The radio-frequency (RF) performance of the p-type NiO-pocket based β-Ga₂O₃/black phosphorous heterostructure MOSFET has been evaluated. The key figure of merits (FOMs) for device performance evaluation include the transconductance (g_m) gate dependent intrinsic-capacitances (C_gd and C_gs), cutoff frequency (f_T), gain bandwidth (GBW) product and output-conductance (g_d). Similarly, power-gain (G_p), power added efficiency (PAE), and output power (P_OUT) are also investigated for large-signal continuous-wave (CW) RF performance evaluation. The motive behind the study is to improve the β-Ga₂O₃ MOS device performance along with a reduction in power losses and device associated leakages. To show the applicability of the designed device in RF applications, its RF FOMs are analyzed. With the outline characteristics of the ultrathin black phosphorous layer below the β-Ga₂O₃ channel region, the proposed device results in 1.09 times improvement in f_T, with 0.7 times lower C_gs, and 3.27 dB improved G_P in comparison to the NiO-GO MOSFET. The results indicate that the designed NiO-GO/BP MOSFET has better RF performance with improved power gain and low leakages.     

Statistical inference and Bayesian optimal life-testing plans under Type-II unified hybrid censoring scheme

This article describes the inferential procedures and Bayesian optimal life-testing issues under Type-II unified hybrid censoring scheme. First, the explicit expressions of expected number of failures, expected duration of testing, and Fisher information matrix for the unknown parameters of the underlying lifetime model are derived. Then, using these quantities, the Bayesian optimal life-testing plans are computed in a subsequent section. A cost constraint D-optimal optimization problem has been formulated, and the corresponding solution algorithm is provided to obtain optimal plans. Computational procedures are illustrated through numerical examples.     

N-doped reduced graphene oxide anchored with δTa2O5 for energy and environmental remediation: Efficient light-driven hydrogen evolution and simultaneous degradation of textile dyes

Precipitation assisted facile hydrothermal method has been developed for anchoring nitrogen-doped reduced graphene oxide (NRGO) with tantalum pentoxide (TO) denoted as TO@NRGO. Synthesized materials were characterized using spectroscopic, optical and microscopic techniques and confirm transforming TO from orthorhombic to hexagonal phase (δ) upon anchoring with NRGO. TO@NRGO, TO and NRGO have been evaluated for photocatalytic hydrogen evolution, degradation of Methylene blue (MB) and Rhodamine B (RhB). The enhanced photocatalytic activity was observed in TO@NRGO nanocomposite compared to TO and NRGO due to decreased bandgap (2.5 eV) and increased surface area (312 m² g^?1). TO@NRGO evolved 19,500 µmol g^?1 of hydrogen for 3 h. TO@NRGO showed better degradation efficiency of 94 and 88% at a time of 100 min for MB and RhB, respectively. The parameters involved in photocatalytic dye degradation, like the effect of pH, catalyst dosage, and initial concentration of dyes, have been carefully optimized to achieve maximum performance of the catalyst. The stability and reusability of TO@NRGO are good and managed to degrade dyes effectively even after the 5th run. Thus, TO@NRGO could serve as a choice of material in resolving the issues related to energy and the environment.     

A finite element modeling-based approach to predict vibrations transmitted through different body segments of the operator within the workspace of a small tractor

Agricultural tractor drivers are subjected to high levels of whole-body vibrations and hand arm vibrations during most part of the farm activities due to unevenness of field surface, uneasy posture, improper workplace design, moving parts of the tractor, and other unavoidable circumstances. The comfort level of the operator inside a dynamic tractor is dependent on the level of vibration generated inside the different human body segments. In the present study, a finite element modeling was proposed to predict vertical vibrations (Z-axis) and frequencies at the different body segments of the seated small tractor operator. The forces required for different controls of the tractor were measured to be used as input parameters in the finite element modeling. The maximum mean forces of the brake (172.8 N) and clutch (153.2 N) were used as the input parameters for the simulation study. The simulated results were validated with the field measured values of vertical accelerations at selected body segments of the operator. The simulation could successfully predict vertical vibrations at selected points of interest (i.e., foot, leg, thigh, lower arm, upper arm, back, and head) except the chest of the body, as the buttock of the operator model was fixed (degree of freedom is equal to zero) in the simulation. The obtained results were compared with the international standards ISO 2631-1 (1985/1997) and ISO 5349-1 (2001) to assess the vibration characteristics at the different body segments of the operator. The foot, leg, lower arm, and upper arm of the operator were subjected to vertical vibration frequencies from 10 to 200 Hz. Most of the resonance of vertical accelerations occurred in one-third octave bands of 20–80 Hz frequencies. The thigh, chest, back, and head of the operator were exposed to vibration frequencies below 40 Hz during field operation. At these parts of the body, the vertical acceleration resonated at lower frequencies, between 2 and 8 Hz.     

Influence of thermophoretic deposition and viscous dissipation on magnetohydrodynamic flow with variable viscosity and thermal conductivity

In this study, we numerically explore the impact of varying viscosity and thermal conductivity on a magnetohydrodynamic flow problem over a moving nonisothermal vertical plate with thermophoretic effect and viscous dissipation. The boundary conditions and flow-regulating equations are converted into ordinary differential equations with the aid of similarity substitution. The MATLAB bvp4c solver is used to evaluate the numerical solution of the problem and it is validated by executing the numerical solution with previously published studies. The impacts of several factors, including the magnetic parameter, Eckert number, heat source parameter, thermal conductivity parameter, stratification parameter, Soret, Dufour, Prandtl number, and Schmidt number are calculated and shown graphically. Also, the skin friction coefficient, Nusselt number, and Sherwood number are calculated. Fluid velocity, temperature, and concentration significantly drop as the thermophoretic parameter and thermal stratification parameter increases. As thermal conductivity rises, it is seen that the velocity of the fluid and temperature inside the boundary layer rise as well. Also, the Soret effect drops temperature and concentration profile. The applications of this type of problem are found in the processes of nuclear reactors, corrosion of heat exchangers, lubrication theory, and so forth.     

Addressing image and Poisson noise deconvolution problem using deep learning approaches

Digital images are more important in numerous contemporary applications, and the need for images in the technical field is also increasing drastically. It is used to recognize signatures and faces in many industries and is applicable for intelligent departments. The images are usually associated with the noise content; this may happen due to the instrument imperfections, troubleshooting while collecting data from the acquisition process, and another natural phenomenon. Poisson noise, also known as photon noise, is caused in the images due to the statistical essence of electromagnetic waves. X-ray, visible light, and gamma rays are electromagnetic waves. The enhancement of the convolution model in addressing images is challenging due to the various constituents such as optical aberrations, noise level, and optical setup. The modeling configuration of the image is attained using the point spread function (PSF), which is responsible for the system's impulse response. The quality image is retrieved by denoising and super-resolution (SR) methods; these methods simultaneously eliminate the noise content from the images. A Richardson–Lucy and alternating direction method of multipliers type of non-blind iterative algorithmic approaches associated with the PSF performance in addressing image is comparatively analyzed. The deep learning approach, convolutional neural networks (CNNs), is also employed to understand the nonlinear mapping relationship between the observed data and ground reality. The performance of the various network approaches is compared in this article. The result obtained shows that the deep learning CNNs achieved higher accuracy in producing denoising images. The goal of the proposed system model is to remove the interference noise in images. The high-resolution images are obtained by implementing a SR-based CNN model.     

MPC-based LFC for interconnected power systems with PVA and ESS under model uncertainty and communication delay

In this paper, a cloud-edge-end collaboration-based control architecture is established for frequency regulation in interconnected power systems (IPS). A model predictive control (MPC)-based load frequency control strategy for the IPS with photovoltaic aggregation and energy storage systems under model uncertainty and communication delay is proposed. This can efectively overcome the issues of model uncertainty, random load perturbation and communication delay. First, a state space model for the IPS is constructed. To coordinate the frequency and contact line power fuctuation of the IPS, a robust controller based on the theory of MPC is then designed. Then, considering the communication delay of frequency response commands during transmission, a predictive compensation mechanism is introduced to eliminate the efect of delay while considering model uncertainty. Finally, simulation results verify the efectiveness and robustness of the proposed control strategy.     

Enhanced Photo Catalytic Activity of ZnO Nano Particles Co-doped with Rare Earth Elements (Nd and Sm) Under UV Light Illumination

Journal of Inorganic and Organometallic Polymers and Materials - In the present report, synthesis of zinc oxide (ZnO) nano particles (NPs) in pure form, 1 wt% of neodymium (Nd)-doped and 1 wt% of...     

A Review on Biogenic Synthesis of Selenium Nanoparticles and Its Biological Applications

In recent decades, aquaculture and environment plays a noteworthy role in rewarding the massive stipulate in all industries. Environmental damage and disease domination are seen as essential issues in the region. In addition to these, nanotechnology as a fresh and imaginative instruments were extremely feasible in aquaculture and environmental applications. Next-generation biological applications of these nanomaterials might lead to an explosion in the bio industries. In order to utilizing the nanoparticles of biogenic expansion, selenium has plays major role in the biological progresses. Selenium (Se) is a multifunctional trace element. The present review analytically intends to the potential biological applications of biosynthesized selenium nanoparticles (SeNPs). Synthesis of SeNPs physical, chemical and biological methods has been used. Physical and chemical methods of SeNPs have high cost, non ecofriendly, highly time consuming. Therefore, there is a growing concern to develop eco friendly and sustainable methods for biosynthesis. Biosynthesis method has ecofriendly, low cost, nontoxic and zero contamination. Biosynthesis of selenium nanoparticles by plant extracts, bacteria, protein, biopolymers, seaweed extracts, fungi and yeasts have used for capping or stabilizing agents. Therefore this review represented original evidence for antibacterial, antifungal, antibiofilm, antioxidant, anticancer, antidiabetic, antimosquito larvicidal and aquaculture applications of prospective biogenic SeNPs were provided in turn in this regard of literatures. Bio synthesis of SeNPs and it is used for many applications like medical, environmental and aquaculture applications. In this review study, the importance of selenium nanoparticles as a competitive element for sustainable aquaculture and environmental applications is also examined in detail.

     

Legendre wavelet method for solving variable-order nonlinear fractional optimal control problems with variable-order fractional Bolza cost

This paper deals with a numerical method for solving variable-order fractional optimal control problem with a fractional Bolza cost composed as the aggregate of a standard Mayer cost and a fractional Lagrange cost given by a variable-order Riemann–Liouville fractional integral. Using the integration by part formula and the calculus of variations, the necessary optimality conditions are derived in terms of two-point variable-order boundary value problem. Operational matrices of variable-order right and left Riemann–Liouville integration are derived, and by using them, the two-point boundary value problem is reduced into the system of algebraic equations. Additionally, the convergence analysis of the proposed method has been considered. Moreover, illustrative examples are given to demonstrate the applicability of the proposed method.