With the development of artificial intelligence and machine learning technology, online shopping is increasingly becoming the mainstream shopping method. It is challenging to investigate the correlation between price dispersion and bargaining. In accordance with the assimilation-contrast theory, this study theorizes about the influence of price dispersion on consumers’ bargaining power and it aims to analyze the moderating role of online shopping experience of consumers. Therefore, this paper develops a discrete price model to measure the dispersion of product prices in the marketplace. Cluster analysis was subsequently conducted to preliminary explore the association between price dispersion and bargaining. With the construction of a multiple regression model, the phenomena found by the cluster analysis are further analyzed. The results indicate that the bargaining power of consumers increases with the increase of the price dispersion level. At high levels of the price dispersion, high-experienced consumers exhibit greater bargaining power than low-experienced consumers. At low levels of price dispersion, low-experienced consumers have higher bargaining power than high-experienced consumers. Finally, we demonstrate the validity and robustness of the study results by grouping regressions and replacing four measures of price dispersion.

Zongwei Li

Lingling Ren

Zhenyu Li

Jianing Chen

Xu Tian

Yanhui Zhang

Price Dispersion, Bargaining Power, and Consumers’ Online Shopping Experience in e-Commerce: Evidence from Online Transactions

Mathematical Problems in Engineering

Retracted: Optimal Coordinated Energy Management in Active Distribution System with Battery Energy Storage and Price-Responsive Demand

Air strikes are among the main means of attack in modern warfare. To improve air defense capabilities and aid military decision-making, threat assessment models have been introduced. As the parameters of the kernel extreme learning machine (KELM) model need to be set individually, this study proposes a parameter learning strategy based on a multistrategy improved sparrow search algorithm (MISSA). First, a reasonable threat assessment model was established based on the capability and situation factors of air targets. Second, the sparrow search algorithm was improved in terms of population position initialization and position update strategy, incorporating tent chaos reverse learning, nonlinear inertia weights, a global search strategy, and adaptive t-distribution. The effectiveness of the MISSA strategy was verified using nine common benchmark functions. The results show that the proposed MISSA finds an effective balance between global and local searches. Moreover, when the MISSA is applied to solve the tuning problem of KELM, the values of mean absolute percentage error, mean square error, root mean square error, and mean absolute error for MISSA–KELM in the air target threat assessment problem are 2.013 × 10−2, 1.282 × 10−4, 1.132 × 10−2, and 8.316 × 10−3, respectively, all of which are higher than that of the other metaheuristic algorithms (e.g., ACWOA-KELM and HGWO-KELM). Therefore, the method proposed in this study can be used as a parameter-tuning tool for KELM, enabling KELM to perform better in practical applications.

Ruiqi Song

Bailin Liu

Suqin Xue

Hong Li

Jingyi Li

Zehua Zhang

Air Target Threat Assessment: A Kernel Extreme Learning Machine Based on a Multistrategy Improved Sparrow Search Algorithm

The magnetic gradient tensor system configured by fluxgate magnetometers is subjected to different scale factors, three-axis nonorthogonality, bias, and misalignment errors. All those errors above will influence the measurement precision directly, so the magnetic gradient tensor system must be calibrated before use. In this paper, an error calibration method of the magnetic gradient tensor system is proposed. The procedure of the proposed method is as follows. Firstly, the error calibration model of the single fluxgate magnetometer is established and generated an ellipsoid mathematical expression. To simplify the calculation, the ellipsoid mathematical expression is transformed into a linear model of intermediate variables, and then, error parameters are estimated by the total least-squares method. Secondly, the orthogonal procrustes problem is adopted to calibrate misalignment errors. Finally, simulations and experiments with a cross magnetic gradient tensor system are carried out for verification of the proposed error calibration method. Results show that compared with the original least-squares method, the proposed method can increase the measurement accuracy of the cross magnetic gradient tensor system greatly.

Cheng Chi

Dan Wang

Ronghua Tao

Zhentao Yu

Error Calibration of Cross Magnetic Gradient Tensor System with Total Least-Squares Method

The development of underground space is fast because of the lack of space. To build shafts in underwater tunnels, the vertical tunneling method (VTM) was invented in 1974 in China, which can act as a freshwater intake or sewage outlet. During the operation of the VTM, the jacking force is one of the essential factors that draw attention. This paper conducts a numerical study of the jacking force and its influencing factors during the vertical tunneling process. First, based on the finite element software ABAQUS, a numerical model of the vertical tunneling process is established according to the VTM project in Beihai, China. Second, in accordance with the Latin hypercube sampling method and the multivariate significance analysis, the mechanical parameters are determined or back-analyzed. Then, the calculated jacking force of the numerical model is compared with the measured jacking force. It turns out that the changing trend of the jacking force in the numerical model and the measured jacking force is relatively consistent. Finally, the influencing factors of the jacking force, such as elastic modulus and the angle of internal friction, are analyzed based on the numerical model. The results show that the elastic modulus and the angle of internal friction of soil are the main influencing factors of the jacking force. The secondary factors are Poisson’s ratio, static earth pressure coefficient, unit weight, and cohesion.

Xinjiang Wei

Xiao Wang

Gang Wei

Liangliang Lu

Numerical Simulation Analysis of the Jacking Force and Its Influencing Factors during the Vertical Tunneling Process

The market scale of electric shavers in China has reached ¥ 26.3 billion in 2021. Consumers currently place an increasing emphasis on the Kansei image conveyed by products rather than just concerning with functional satisfaction. To meet consumers’ expectations, the emotional message conveyed by product design is essential under multisensory channels. This research first collected 230 electric shavers samples and 135 pairs of consumers’ Kansei words, then reduced them into 34 representative samples using multidimensional scale and clustering analysis, with 4 groups of representative Kansei words selected via the expert group. Moreover, consumers’ Kansei images were evaluated via questionnaire using the semantic differential scales, with 416 valid samples acquired in total. Meanwhile, design elements of the samples (including item and category) were classified by ways of morphological analysis and audio software. At last, the prediction models of the electric shavers were established between the overall design elements and user’s Kansei evaluation under the multisensory channel of visual model and auditory audio taking advantage of Quantification Theory Type I , back propagation neural network, and genetic algorithm-based BPNN. The proposed models can provide defined design indexes and references in multisensory design, facilitating designers to design in a logical and scientific manner rather than designing as per experience.

Zhe-Hui Lin

Jeng-Chung Woo

Feng Luo

Guo-Qing Pan

Multisensory Design of Electric Shavers Based on Kansei Engineering and Artificial Neural Networks

The port groups of the Guangdong-Hong Kong-Macao Greater Bay Area faces a new competitive and cooperative game structure concerning global competition, making it is necessary to build a comprehensive port coordination and development mechanism. In this study, we apply the logistics model to conduct parameter estimation and evolution trend analysis on the development of the port group and highlight that the current container throughput of the Guangdong-Hong Kong-Macao Greater Bay Area port group will reach the maximum and the new impetus is needed to push it into the next round of development and evolution. Combined with the theory of ecological population, the Lotka–Volterra model of multiple groups is introduced to study competition and cooperation game, explore their interaction, and provide quantitative support for the formation of different central port groups in the Guangdong-Hong Kong-Macao Greater Bay Area. The study provides references for strengthening the construction of complementary resources and coordinated mechanism within the port group, realizing the differential development between ports in the Guangdong-Hong Kong-Macao Greater Bay Area.

Shan Liang

Xuanfei Wang

Zhenjie Liao

Game Analysis of Container Ports Co-Competition and Coordinated Development in Guangdong-Hong Kong-Macao Greater Bay Area

This paper proposes an efficient short-time probability approximation with Lévy excitation to capture the transient probability distribution and its evolving path. Using principal component analysis (PCA), the method constructs a probability core to exclude outliers beyond it. The statistics of samples that fall inside the core are treated, with a prescribed fiducial probability, as an easy-to-estimate Gaussian type. The idea is verified numerically by compared with Monte-Carlo results. Then, it is integrated into the path integral (PI) method, combined with evolving probabilistic vector (EPV) techniques, to efficiently obtain probability distributions in each time step of PI. This scheme is semianalytical, only dependent on a relatively small amount of response samples to form the probability core; thus, it can have very computational advantages over full Monte-Carlo simulation to capture transient responses and probability distributions. The application to investigating response transitions of a nonsmooth system driven by Lévy shock and jump has revealed the performance of the proposed method. Also, the exit times of stochastic response are characterized quantitatively from the perspective of global dynamic transition. These investigations will be helpful to achieve the efficient probability estimation for nonlinear system with non-Gaussian inputs and quantify the reliability of the mechanical system.

Zigang Li

Wang Yan

Jiaqi Kang

Ming Li

A Short-Time Non-Gaussian Probability Approximation Based Method for Response Transitions of a Lévy-Driven Nonsmooth System

About this journal

Mathematical Problems in Engineering is a broad-based journal publishing&nbsp;results of rigorous engineering research across all disciplines, carried out using mathematical tools.

Journal profile

Meet the editorial board

Chief Editor, Professor Guangming Xie, is currently a full professor of dynamics and control with the College of Engineering, Peking University. His research interests include complex system dynamics and control and intelligent and biomimetic robots.

Editor spotlight

Submit to Special Issue

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Special Issues

Author guidelines

Editorial board

Databases and indexing

Mathematical Problems in Engineering - Table of contents 2023

Mathematical Problems in Engineering | Hindawi

title

Mathematical Problems in Engineering is a broad-based journal publishing results of rigorous engineering research across all disciplines, carried out using mathematical tools.

description

https://images.hindawi.com/journals/mpe/mpe.banner.jpg

twitter:card

twitter:description

twitter:site

twitter:title

twitter:url

twitter:image