Purpose -Top managers who possess outstanding leadership competence make significant contributions toward achieving project success. The relationship between the top managers' leadership and infrastructure sustainability (IS), one of the most important project success criteria, is empirically researched in this study. The purpose of this paper is to explore relationships between top managers' leadership competence of intellectual competence (IQ), managerial competence (MQ), and emotional and social competence (EQ) and to explore relationships between leadership competence and IS. Design/methodology/approach -Based on 246 obtained responses to a questionnaire survey across infrastructure projects in the context of the Chinese urbanization process, the analysis of the relationships between top managers' leadership and IS was performed using a structural equation model (SEM). Findings -Results indicate that top managers' leadership competence, with MQ being the main determinant, followed by IQ, directly drives the entire life cycle of an infrastructure project toward accomplishing IS. Through positively influencing the moderate variable of MQ, EQ competence is found to have an indirect influence on IS. Practical implications -In terms of practical implications, the outcomes of this research will provide criteria for the selection of top managers for infrastructure projects to realize IS during the process of Chinese urbanization. Originality/value -The established SEM improves the leadership competence framework of IQ, MQ, and EQ in the respect of reflecting the context of infrastructure projects and promotes the research and development of leadership theory in the construction area.

High-resolution range profile is the significant characteristic of radar targets in automatic target recognition. Traditional feature extractions of range profiles in target classification are constrained to the original scale. This Letter proposes a multi-scale target classification method based on the scale-space theory. Target range profile feature is extended from single scale to multiple scales. The minimum Kullback-Leibler mean divergence (MKMD) algorithm is developed to achieve the automatic optimal scale factor selection. Classification evaluations on aircraft models using support vector machine and 3nearest neighbour classifiers demonstrate that the application of scale-space theory in multi-scale feature extraction could effectively enhance the classification performance. The feasibility of the proposed MKMD algorithm is also validated by an enumeration method.

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Based on measurements from a near-field scanner and far-field measurements obtained in a semi-anechoic chamber, a statistical relationship is established between a magnetic field in the near field and an electric field in the far field. The relationship makes it possible to transform a radiated-emission regulatory limit from the far-field to the near-field zone. The transformed near-field limit can allow efficient prediction of radiated-emission compliance for high-speed printed circuit boards. The presented results demonstrate the feasibility of the proposed method for a quick radiated-emission pre-compliance check without heavy equipment investment.