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The purpose of this paper is to exploit a database of pre-existing designs to accelerate parametric optimization of antenna structures is investigated.

The usefulness of pre-existing designs for rapid design of antennas is investigated. The proposed approach exploits the database existing antenna base designs to determine a good starting point for structure optimization and its response sensitivities. The considered method is suitable for handling computationally expensive models, which are evaluated using full-wave electromagnetic (EM) simulations. Numerical case studies are provided demonstrating the feasibility of the framework for the design of real-world structures.

The use of pre-existing designs enables rapid identification of a good starting point for antenna optimization and speeds-up estimation of the structure response sensitivities. The base designs can be arranged into subsets (simplexes) in the objective space and used to represent the target vector, i.e. the starting point for structure design. The base closest base point w.r.t. the initial design can be used to initialize Jacobian for local optimization. Moreover, local optimization costs can be reduced through the use of Broyden formula for Jacobian updates in consecutive iterations.

The study investigates the possibility of reusing pre-existing designs for the acceleration of antenna optimization. The proposed technique enables the identification of a good starting point and reduces the number of expensive EM simulations required to obtain the final design.

The proposed design framework proved to be useful for the identification of good initial design and rapid optimization of modern antennas. Identification of the starting point for the design of such structures is extremely challenging when using conventional methods involving parametric studies or repetitive local optimizations. The presented methodology proved to be a useful design and geometry scaling tool when previously obtained designs are available for the same antenna structure.

A novel framework for expedited antenna optimization with an iterative prediction-correction scheme is proposed. The methodology is comprehensively validated using three real-world antenna structures: narrow-band, dual-band and wideband, optimized under various design scenarios.

The keystone of the proposed approach is to reuse designs pre-optimized for various sets of performance specifications and to encode them into metamodels that render good initial designs, as well as an initial estimate of the antenna response sensitivities. Subsequent design refinement is realized using an iterative prediction-correction loop accommodating the discrepancies between the actual and target design specifications.

The presented framework is capable of yielding optimized antenna designs at the cost of just a few full-wave electromagnetic simulations. The practical importance of the iterative correction procedure has been corroborated by benchmarking against gradient-only refinement. It has been found that the incorporation of problem-specific knowledge into the optimization framework greatly facilitates parameter adjustment and improves its reliability.

The proposed approach can be a viable tool for antenna optimization whenever a certain number of previously obtained designs are available or the designer finds the initial effort of their gathering justifiable by intended re-use of the procedure. The future work will incorporate response features technology for improving the accuracy of the initial approximation of antenna response sensitivities.

The proposed optimization framework has been proved to be a viable tool for cost-efficient and reliable antenna optimization. To the knowledge, this approach to antenna optimization goes beyond the capabilities of available methods, especially in terms of efficient utilization of the existing knowledge, thus enabling reliable parameter tuning over broad ranges of both operating conditions and material parameters of the structure of interest.

The authors provide a comprehensive empirical examination on the impact of earnings quality on stock price crash risk in China.

The authors acknowledge and distinguish two-dimensional proxies for earnings quality – accounting-based (earnings management degree) and market-based (earnings transparency) known in accounting and finance literature.

The authors find that both generally indicate that better earnings quality is associated with less crashes. However, extremely high earnings transparency interacted with insider trading profit can also actually exacerbate stock price crashes.

This study is the first to highlight the pertinence of accounting-based measures to proxy for earnings quality in a fast-growing emerging market environment such as China.

The purpose of this paper is make a significant contribution to the supply chain knowledge system through research on modern supply chain system in China, providing guidance for theoretical research such as methodology of dynamic resource allocation and application of innovative small- and middle-sized service system in the supply chain.

The paper uses structural analysis of Chinese competitive advantage, and it applies comparative analysis of supply chain models in China, the USA and Japan through the factor disintegration of trading environment.

China’s supply chain model has virtual scale and virtual capabilities. The relationship with suppliers is more dynamic. The requirements for resolving uncertainty are higher. Business transfer is more frequent.

Researchers are encouraged to propose the specific supply chain models in China further with the game theory, auction theory, etc.

It provides advice for government policy making and gives Chinese enterprises guidance to improve operation management.

This paper specifically analyzes characteristics of China supply chain and gives enlightenment for supply chain innovation.