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This paper aims to propose a noise-robust method to estimate the frequency of the reflective echo to reduce the negative effects of noise and improve the accuracy and resolution of a resonant surface acoustic wave (SAW) sensor.

The proposed approach exploits the singular value decomposition to obtain the frequency information of a SAW response signal and overcome the noise influences.

Compared with the commonly used Fourier transform (FT) method, the accuracy and resolution improvement of the proposed method used in the SAW sensor is validated.

The system using the proposed method delivers lesser standard deviation, that is, delivers higher performance than the conventional system using the fast FT method.

This paper aims to present an context evaluation and frequency measurement method for surface acoustic wave (SAW) resonant sensor.

This method is based on a signal subspace construction, which, along with assembling optional value set, provides the results.

The method can assess the application context and improve the resolution and accuracy of the passive wireless SAW resonator sensor system.

Passive wireless SAW resonators have been used as sensor elements for different physical parameters such as temperature, pressure and force in a number of industrial and medical applications. Various wireless channel environments introduce different application contexts.

Fashion merchandising hold a paramount position within the realm of retail marketing. Currently, the purpose of this article is that the assessment of display effectiveness predominantly relies on the subjective judgment of merchandisers due to the absence of an effective evaluation method. Although eye-tracking devices have found extensive used in tracking the gaze trajectory of subject, they exhibit limitations in terms of stability when applied to the evaluation of various scenes. This underscores the need for a dependable, user-friendly and objective assessment method.

To develop a cost-effective and convenient evaluation method, the authors introduced an image processing framework for the assessment of variations in the impact of store furnishings. An optimized visual saliency methodology that leverages a multiscale pyramid model, incorporating color, brightness and orientation features, to construct a visual saliency heatmap. Additionally, the authors have established two pivotal evaluation indices aimed at quantifying attention coverage and dispersion. Specifically, bottom features are extract from 9 distinct scale images which are down sampled from merchandising photographs. Subsequently, these extracted features are amalgamated to form a heatmap, serving as the focal point of the evaluation process. The authors have proposed evaluation indices dedicated to measuring visual focus and dispersion, facilitating a precise quantification of attention distribution within the observed scenes.

In comparison to conventional saliency algorithm, the optimization method yields more intuitive feedback regarding scene contrast. Moreover, the optimized approach results in a more concentrated focus within the central region of the visual field, a pattern in alignment with physiological research findings. The results affirm that the two defined indicators prove highly effective in discerning variations in visual attention across diverse brand store displays.

The study introduces an intelligent and cost-effective objective evaluate method founded upon visual saliency. This pioneering approach not only effectively discerns the efficacy of merchandising efforts but also holds the potential for extension to the assessment of fashion advertisements, home design and website aesthetics.

Selective laser melting (SLM) is a powder metallurgical (PM) additive manufacturing process whereby a three‐dimensional part is built in a layer‐wise manner. During the process, a high intensity laser beam selectively scans a powder bed according to the computer‐aided design data of the part to be produced and the powder metal particles are completely molten. The process is capable of producing near full density (∼98‐99 per cent relative density) and functional metallic parts with a high geometrical freedom. However, insufficient surface quality of produced parts is one of the important limitations of the process. The purpose of this study is to apply laser re‐melting using a continuous wave laser during SLM production of 316L stainless steel and Ti6Al4V parts to overcome this limitation.

After each layer is fully molten, the same slice data are used to re‐expose the layer for laser re‐melting. In this manner, laser re‐melting does not only improve the surface quality on the top surfaces, but also has the potential to change the microstructure and to improve the obtained density. The influence of laser re‐melting on the surface quality, density and microstructure is studied varying the operating parameters for re‐melting such as scan speed, laser power and scan spacing.

It is concluded that laser re‐melting is a promising method to enhance the density and surface quality of SLM parts at a cost of longer production times. Laser re‐melting improves the density to almost 100 per cent whereas 90 per cent enhancement is achieved in the surface quality of SLM parts after laser re‐melting. The microhardness is improved in the laser re‐molten zone if sufficiently high‐energy densities are provided, probably due to a fine‐cell size encountered in the microstructure.

There has been extensive research in the field of laser surface modification techniques, e.g. laser polishing, laser hardening and laser surface melting, applied to bulk materials produced by conventional manufacturing processes. However, those studies only relate to laser enhancement of surface or sub‐surface properties of parts produced using bulk material. They do not aim at enhancement of core material properties, nor surface enhancement of (rough) surfaces produced in a PM way by SLM. This study is carried out to cover the gap and analyze the advantages of laser re‐melting in the field of additive manufacturing.

This study aims to examine the effects of dialect connectedness between the chairman and the chief executive officer (CEO) (DCCC) on the tunneling activities of controlling shareholders.

This study uses abnormal related-party transactions (ARPT) as a proxy for tunneling activities and traces dialects of chairmen and CEOs based on the respective birthplace information. Baseline results are examined using a fixed-effects model. The results remain robust when using the instrumental variable approach, propensity score matching (PSM) technique, changing the measurement of tunneling and Heckman two-step selection model.

The results show that DCCC reduces tunneling activities. This negative association is more pronounced for non-state-owned enterprises and firms whose chairmen and CEOs work in the respective hometowns. DCCC restrains tunneling activities through mechanisms by establishing an informal supervisory effect on CEOs because the CEOs fear reputational damage and strengthening cooperation between chairmen and CEOs. Further analyses suggest that this negative association is more significant when chairmen and CEOs are non-controlling shareholders, but the association is weakened during the coronavirus disease 2019 (COVID-19) crisis.

As dialect is a carrier of culture, this study's results imply that cultural proximity can replace formal mechanisms to enhance corporate governance.