Applied Science Private University

About: Applied Science Private University is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Catalysis & Population. The organization has 4124 authors who have published 5299 publications receiving 116167 citations.

Evidence of Nonlocal Breakdown of the Integer Quantum Hall Effect

Abstract: Current induced breakdown of the integer quantum Hall effect (QHE) is studied in GaAs $/$AlGaAs single heterostructure Hall bars at $T\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}1.6$--4.2 K and $B\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}2$--6 T ( $\ensuremath{ u}\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}2$, 4, and 6). The QHE breakdown is absent over a macroscopic region in the two-dimensional electron gas channel on the side of the electron-injecting corner of the Hall bars. The observed nonlocal nature suggests that bootstrap-type electron heating is relevant to the QHE breakdown.

High-Dimensional Neural-Network Technique and Applications to Microwave Filter Modeling

Abstract: Neural networks are useful for developing fast and accurate parametric model of electromagnetic (EM) structures. However, existing neural-network techniques are not suitable for developing models that have many input variables because data generation and model training become too expensive. In this paper, we propose an efficient neural-network method for EM behavior modeling of microwave filters that have many input variables. The decomposition approach is used to simplify the overall high-dimensional neural-network modeling problem into a set of low-dimensional sub-neural-network problems. By incorporating the knowledge of filter decomposition with neural-network decomposition, we formulate a set of neural-network submodels to learn filter subproblems. A new method to combine the submodels with a filter empirical/equivalent model is developed. An additional neural-network mapping model is formulated with the neural-network submodels and empirical/equivalent model to produce the final overall filter model. An H -plane waveguide filter model and a side-coupled circular waveguide dual-mode filter model are developed using the proposed method. The result shows that with a limited amount of data, the proposed method can produce a much more accurate high-dimensional model compared to the conventional neural-network method and the resulting model is much faster than an EM model.

Development of an interval-valued fuzzy linear-programming method based on infinite α-cuts for water resources management

Abstract: An interval-valued fuzzy linear-programming (IVFL) method based on infinite @a-cuts is developed for water resources management in this study. The introduction of interval parameters and interval-valued fuzzy parameters into the objective function and constraints makes it possible for dealing with individual uncertainty and dual uncertainties existing in many real-world cases. A two-step infinite @a-cuts (TSI) solution method is communicated to the solution process to discretize infinite @a-cuts to interval-valued fuzzy membership functions. Application to an agricultural irrigation problem indicates that interval-valued fuzzy sets can represent dual uncertainties in modeling parameters, and the solution method is able to generate decisions with enhanced reliability. It is also indicated that the objective (i.e. system net benefit) can be increased with the growth of violation risk, in association with a set of different allocation schemes. As the key segment of interval-valued fuzzy membership functions that could significantly affect system performance can be identified through the analysis of decision alternatives under different risk levels of constraint violation, the IVFL method provides decision makers flexibility in selecting an appropriate decision scheme according to their preference and practical conditions.