Influence of Si-Doping on the Performance of InGaN/GaN Multiple Quantum Well Solar Cells

Semiconductors - The performance of InGaN/GaN multiple quantum well (MQW) solar cells with five different Si-doping concentrations, namely 0, 4 × 1017 cm–3, 1 × 1018 cm–3, 3...     

Trend Analysis and Spatial Prediction of Groundwater Levels Using Time Series Forecasting and a Novel Spatio-Temporal Method

Water Resources Management - Overexploitation of groundwater in the Malayer Plain has resulted in a continuous decline of groundwater levels over recent years with associated risks to water...     

Mixed-Metal MOF-74 Templated Catalysts for Efficient Carbon Dioxide Capture and Methanation

The vast chemical and structural tunability of metal–organic frameworks (MOFs) are beginning to be harnessed as functional supports for catalytic nanoparticles spanning a range of applications. However, a lack of straightforward methods for producing nanoparticle-encapsulated MOFs as efficient heterogeneous catalysts limits their usage. Herein, a mixed-metal MOF, NiMg-MOF-74, is utilized as a template to disperse small Ni nanoclusters throughout the parent MOF. By exploiting the difference in Ni O and Mg O coordination bond strength, Ni²⁺ is selectively reduced to form highly dispersed Ni nanoclusters constrained by the parent MOF pore diameter, while Mg²⁺ remains coordinated in the framework. By varying the ratio of Ni to Mg in the parent MOF, accessible surface area and crystallinity can be tuned upon thermal treatment, influencing CO₂ adsorption capacity and hydrogenation selectivity. The resulting Ni nanoclusters prove to be an active catalyst for CO₂ methanation and are examined using extended X-ray absorption fine structure and X-ray photoelectron spectroscopy. By preserving a segment of the Mg²⁺-containing MOF framework, the composite system retains a portion of its CO₂ adsorption capacity while continuing to deliver catalytic activity. The approach is thus critical for designing materials that can bridge the gap between carbon capture and CO₂ utilization.     

An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

Throughput performance of NOMA in WLANs with a CSMA MAC protocol

Wireless Networks - The existing medium access control (MAC) protocols are not able to utilize the full opportunities from power-domain non-orthogonal multiple access (NOMA) technique in wireless...     

Electrodynamics of Inhomogeneous 2D Periodic Media

Journal of Communications Technology and Electronics - A new method is proposed to represent electromagnetic field in inhomogeneous 2D periodic medium (PM) as a discrete set of amplitude vectors...     

Systematic convergence of nonlinear stochastic estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group with the tracking error of the normalized Euclidean distance meeting predefined transient and steady‐state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low‐cost inertial measurement units. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form.     

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility. In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the “readiness to fail” of a subsystem in conjunction with the “failure riskiness” can determine the “failure criticality.” The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design‐out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system.