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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.

In this study, the Models-3/Community Multiscale Air Quality Model (CMAQ) coupled with the Mesoscale Modeling System Generation 5 (MM5) was employed to simulate the air pollution episodes over East Asia, northern China Plain (NCP), and southern Hebei (SHB), at a grid resolution of 36, 12, and 4 km, respectively in Oct. 2012. The PM₁₀ concentrations over SHB at 12-km are overpredicted with NMBs of 34.6% to 45.7% and also overestimated with that of 72.1% to 97.5% at 4-km which applied such a fine grid resolution over the SHB for the first time. It indicated that the simulation at 12-km performs better than the 4-km which may be related to the spatial allocation of the emissions, the lack of dust emissions and the limitations of model treatments. Five heavy episodes show the characteristics of sawtooth-shaped cycles over the NCP in fall (i.e. the maximum of PM₁₀ was up to 885.1 µg m⁻³ and PM_2.5 was up to 438.4 µg m⁻³ in Handan city) which resulting in the deterioration of visibility and periodically haze days. The concentrations of OC, EC, SO₄²⁻, NO₃⁻ and NH₄⁺ were significantly higher in heavy episodes than non-heavy pollution episodes. In comparison with other cities ([NO₃⁻]/[SO₄²⁻] > 1) at 12-km, the monthly-mean mass ratio of [NO₃⁻]/[SO₄²⁻] at Taiyuan (0.17−0.73), Shijiazhuang (0.28−2.34) was 0.43, 0.84 respectively, which means the stationary sources emissions were more important than the vehicle emission in the source areas. The influence of the regional transportation for pollutants compared with local emission was also an important factor for heavy pollution episodes. The regional joint framework should be established along with controlling the local emission over the SHB in China to improve the air quality.

Effectiveness of the homogenization method for various heat transfer problems of engineering composites is the main aim of the paper. This comparative study is done for layered, fiber and particle reinforced Representative Volume Elements (RVE) for composites made of widely used components. Mathematical model is based on the effective modules method introduced for periodic composites ‐ effective heat conductivity is calculated in the closed form for specific spatial distribution of the components, while effective volumetric heat capacity is obtained from a simple spatial averaging. Such a homogenization scheme makes possible to significantly simplify the numerical analysis of transient heat transfer phenomena in various types of composites. The comparison of temperature histories obtained for the real and homogenized composite models is carried out using the Finite Element Method system ANSYS. As is demonstrated for various boundary problems, a homogenization technique in terms of composites types collected in the paper give satisfactory agreement with the real structure modeling; further numerical studies on composite cells discretization should increase modeling efficiency and diminish the numerical errors.

The purpose of this paper is to study the buckling and the vibration of the beam induced by atom/molecule adsorption using the nonlocal Euler‐Bernoulli beam model with initial axial stress.

The nonlocal parameter associated with adsorbed mass and bending rigidity variations of the beam induced by adsorbates are taken into account, and the buckling and dynamic behaviors are obtained via the Hamilton's principle, in which the potential energy between adsorbates and surfaces of the beam, the bending energy, the external work and the kinetic energy are summed as the Lagrangian function.

The results show that, for both buckling and resonant frequency, the nonlocal effect should be considered when the beam scales down to several hundreds of nanometres, especially for higher mode numbers.

The present paper gives the exact expressions for the buckling and resonant frequency of a simple‐supported nonlocal beam with initial axial stress. Different from previous works, the mass increasing and bending rigidity of the beam are found size‐dependent (nonlocal effect), resulting in possible different static and dynamic behaviors of the beam when atom/molecular adsorption occurs. The exact expressions obtained for the buckling and resonant frequency may be helpful to the design and application of micro‐ and nanobeam‐based sensors/resonators.

The purpose of this paper is to show how particle scale simulation of industrial particle flows using DEM (discrete element method) offers the opportunity for better understanding of the flow dynamics leading to improvements in equipment design and operation.

The paper explores the breadth of industrial applications that are now possible with a series of case studies.

The paper finds that the inclusion of cohesion, coupling to other physics such fluids, and its use in bubbly and reacting flows are becoming increasingly viable. Challenges remain in developing models that balance the depth of the physics with the computational expense that is affordable and in the development of measurement and characterization processes to provide this expanding array of input data required. Steadily increasing computer power has seen model sizes grow from thousands of particles to many millions over the last decade, which steadily increases the range of applications that can be modelled and the complexity of the physics that can be well represented.

The paper shows how better understanding of the flow dynamics leading to improvements in equipment design and operation can potentially lead to large increases in equipment and process efficiency, throughput and/or product quality. Industrial applications can be characterised as large, involving complex particulate behaviour in typically complex geometries. The critical importance of particle shape on the behaviour of granular systems is demonstrated. Shape needs to be adequately represented in order to obtain quantitative predictive accuracy for these systems.

The purpose is to analyze the mechanical behavior of the arterial wall in the degraded region of the arterial wall and to determine the stress distribution, as an important factor for predicting the potential failure mechanisms in the wall. In fact, while the collagen fiber degradation process itself is not modeled, zones with reduced collagen fiber content (corresponding to the degradation process) are assumed. To do so, a local weakness in the media layer is considered by defining representative volume elements (RVEs) with different fiber collagen contents in the degraded area to investigate the mechanical response of the arterial wall.

A three-dimensional (3D) large strain hierarchical multiscale technique, based on the homogenization and genetic algorithm (GA), is utilized to numerically model collagen fiber degradation in a typical artery. Determination of material constants for the ground matrix and collagen fibers in the microscale level is performed by the GA. In order to investigate the mechanical degradation, two types of RVEs with different collagen contents in fibers are considered. Each RVE is divided into two parts of noncollagenous matrix and collagen fiber, and the part of collagen fiber is further divided into matrix and collagen fibrils.

The von Mises stress distributions on the inner and outer surfaces of the artery and the influence of collagen fiber degradation on thinning of the arterial wall in the degraded area are thoroughly studied. Comparing the maximum stress values on outer and inner surfaces in the degraded region shows that the inner surface is under higher stress states, which makes it more prone to failure. Furthermore, due to the weakness of the artery in the degraded area, it is concluded that the collagen fiber degradation considerably reduces the wall thickness in the degraded area, leading to an observable local inflation across the degraded artery.

Considering that little attention has been paid to multiscale numerical modeling of collagen fiber degradation, in this paper a 3D large strain hierarchical multiscale technique based on homogenization and GA methods is presented. Therefore, while the collagen fiber degradation process itself is not modeled in this study, zones with reduced collagen fiber content (corresponding to the degradation process) are assumed.

The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing.

To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results.

The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models.

The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

Digital twin (DT) is an emerging technology that enables sophisticated interaction between physical objects and their virtual replicas. Although DT has recently gained significant attraction in both industry and academia, there is no systematic understanding of DT from its development history to its different concepts and applications in disparate disciplines. The majority of DT literature focuses on the conceptual development of DT frameworks for a specific implementation area. Hence, this paper provides a state-of-the-art review of DT history, different definitions and models, and six types of key enabling technologies. The review also provides a comprehensive survey of DT applications from two perspectives: (1) applications in four product-lifecycle phases, i.e. product design, manufacturing, operation and maintenance, and recycling and (2) applications in four categorized engineering fields, including aerospace engineering, tunneling and underground engineering, wind engineering and Internet of things (IoT) applications. DT frameworks, characteristic components, key technologies and specific applications are extracted for each DT category in this paper. A comprehensive survey of the DT references reveals the following findings: (1) The majority of existing DT models only involve one-way data transfer from physical entities to virtual models and (2) There is a lack of consideration of the environmental coupling, which results in the inaccurate representation of the virtual components in existing DT models. Thus, this paper highlights the role of environmental factor in DT enabling technologies and in categorized engineering applications. In addition, the review discusses the key challenges and provides future work for constructing DTs of complex engineering systems.

The spatial feature is important for scene saliency detection. Scene-based visual saliency detection methods fail to incorporate 3D scene spatial aspects. This paper aims to propose a cube-based method to improve saliency detection through integrating visual and spatial features in 3D scenes.

In the presented approach, a multiscale cube pyramid is used to organize the 3D image scene and mesh model. Each 3D cube in this pyramid represents a space unit similar to a pixel in the image saliency model multiscale image pyramid. In each 3D cube color, intensity and orientation features are extracted from the image and a quantitative concave–convex descriptor is extracted from the 3D space. A Gaussian filter is then used on this pyramid of cubes with an extended center-surround difference introduced to compute the cube-based 3D scene saliency.

The precision-recall rate and receiver operating characteristic curve is used to evaluate the method and other state-of-art methods. The results show that the method used is better than traditional image-based methods, especially for 3D scenes.

This paper presents a method that improves the image-based visual saliency model.

The purpose of this paper is to discuss a multiscale pricing model for the French stock market by combining wavelet analysis and Fama‐French three‐factor model. The objective is to examine the relationship between stock returns and Fama‐French risk factors at different time‐scales.

Exploiting the scale separation property inherent to the maximal overlap discrete wavelet transform, the data set are decomposed into components associated with different time‐scales. This wavelet‐based decomposition scheme allows the three Fama‐French models to be tested over different investments periods.

The obtained results show that the explanatory power of the Fama‐French three‐factor model becomes stronger as the wavelet scale increases. Besides, the relationship between the portfolio returns and the risk factors (i.e. the market, size and value factors) depends significantly upon the considered time‐horizon.

The proposed methodology offers investors the opportunity to construct dynamic portfolio management strategies by taking into account the multiscale nature of risk and return. Moreover, it gives a new insight to fund rating and fund selection issues in relation to heterogeneous investments periods.

The paper uses wavelets as a relatively new and powerful tool for statistical analysis that allows a new understanding of pricing models. The paper will be of interest not only for academics in the field of asset pricing but also for fund managers and financial market investors.