The concept of coding metasurface makes a link between physically metamaterial particles and digital codes, and hence it is possible to perform digital signal processing on the coding metasurface to realize unusual physical phenomena. Here, this study presents to perform Fourier operations on coding metasurfaces and proposes a principle called as scattering‐pattern shift using the convolution theorem, which allows steering of the scattering pattern to an arbitrarily predesigned direction. Owing to the constant reflection amplitude of coding particles, the required coding pattern can be simply achieved by the modulus of two coding matrices. This study demonstrates that the scattering patterns that are directly calculated from the coding pattern using the Fourier transform have excellent agreements to the numerical simulations based on realistic coding structures, providing an efficient method in optimizing coding patterns to achieve predesigned scattering beams. The most important advantage of this approach over the previous schemes in producing anomalous single‐beam scattering is its flexible and continuous controls to arbitrary directions. This work opens a new route to study metamaterial from a fully digital perspective, predicting the possibility of combining conventional theorems in digital signal processing with the coding metasurface to realize more powerful manipulations of electromagnetic waves.

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This paper presents a physics-of-failure (PoF)-based prognostics and health management approach for effective reliability prediction. PoF is an approach that utilizes knowledge of a product's life cycle loading and failure mechanisms to perform reliability design and assessment. PoF-based prognostics permit the assessment of product reliability under its actual application conditions. It integrates sensor data with models that enable in situ assessment of the deviation or degradation of a product from an expected normal operating condition (ie, the product's 'health') and the prediction of the future state of reliability. A formal implementation procedure, which includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life cycle loads, damage accumulation, and assessment of uncertainty, is presented. Then, applications of PoF-based prognostics are discussed.

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Purpose The purpose of this paper is to explain the “good-to-good” app switching phenomenon that has not been specifically addressed in the prior switching literature. Drawing on the consumer learning theory, this study explores how external social word of mouth (WOM) and internal satisfaction influence app users’ switching intention through social learning route and analogical learning route. This study also examines the moderating effect of app heterogeneity. Design/methodology/approach An online survey was used to collect data. Two categories of mobile apps with different levels of within-category heterogeneity were targeted in survey questions. A total of 525 valid survey responses were collected. Findings Social WOM about a competing app increases users’ switching intention through both social norm influence and social information influence, resulting in a direct effect on switching intention and an indirect effect through the perceived attractiveness of a competing app. Users’ satisfaction with an adopted app positively influences the perceived attractiveness of an unadopted competing app, offering evidence for analogical learning in user switching. Meanwhile, users’ satisfaction imposes a direct negative effect on switching intention. A higher level of within-category heterogeneity strengthens (weakens) the positive effect of social WOM (satisfaction) on users’ perceived attractiveness of a competing app. Originality/value This study complements the existing switching literature by disentangling the “good-to-good” switching phenomenon in the mobile app market from the consumer learning perspective. This study extends the understanding of cross-category user switching by considering different levels of product heterogeneity.

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A novel electrochemiluminescence (ECL) aptasensor for platelet-derived growth factor B chain (PDGF-BB) assay was developed by assembling N-(aminobutyl)-N-ethylisoluminol functionalized gold nanoparticles (ABEI-AuNPs) with aptamers as nanoprobes. In the protocol, the biotinylated aptamer capture probes were first immobilized on a streptavidin coated gold nanoparticle (AuNPs) modified electrode, afterwards, the target PDGF-BB and the ABEI-AuNPs tagged aptamer signal probe were successively attached to the modified electrode by virtue of the dimer structure of PDGF-BB to fabricate a "sandwich" conjugate modified electrode, i.e. an aptasensor. ECL measurement was carried out with a double-step potential in carbonate buffer solution containing H(2)O(2). The aptasensor showed high sensitivity and selectivity toward PDGF-BB and specificity toward PDGF-BB aptamer. The detection limit was as low as 2.7 × 10(-14) M. In this work, the ABEI-AuNPs synthesized by a simple seed growth method have been successfully used as aptamer labels, which greatly amplified the ECL signal by binding numbers of ABEI molecules on the surface of AuNPs. The ABEI-AuNPs signal amplification is superior to other reported signal amplification strategies based on aptamer-related polymerase chain reaction or functionalized nanoparticles in simplicity, stability, labeling property and practical applicability. And the ABEI-AuNPs based nanoprobe is more sensitive than the luminol functionalized AuNPs based nanoprobe. Moreover, such an ultra-sensitive and low-cost assay can be accomplished with a simple and fast procedure by using a simple ECL instrumentation. The aptasensor was also applied for the detection of PDGF-BB in human serum samples, showing great application potential. Given these advantages, the ECL aptasensor is well suited for the direct, sensitive and rapid detection of protein in complex clinical samples.

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