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Entrepreneurs and individual sellers heavily leverage their social ties embedded in social media, expressive or instrumental, to penetrate the market and achieve business success. However, the extant social commerce literature offers limited understanding on how different forms of buyer−seller social ties embedded in social media affect buyers' purchase behaviors. The study draws on the theoretical lens of social ties and proposes an integrative theoretical framework to understand the direct and indirect influences of expressive and instrumental ties (ExTSM and InTSM) between buyers and sellers on buyers' purchase intention (PI) in social commerce.

The authors first validated the measures of ExTSM and InTSM with survey data from 166 Weibo commerce buyers. They then tested their theoretical framework and hypotheses with survey data from 246 buyer−seller dyads in WeChat commerce.

With a buyer-centric view, (1) ExTSM and InTSM, respectively, had a direct negative and a positive influence on PI; (2) both trust and perceived product value displayed inconsistent mediation effects on the negative relationship between ExTSM and PI; and (3) only perceived product value mediated the positive influence of InTSM on PI. From sellers' viewpoint, (1) their ExTSM and InTSM with buyers were mixed up, and (2) the mingled social ties negatively impacted buyers' purchase intention.

The findings of the study advance the theoretical understanding of social commerce and offer practical guidance for small and medium-sized enterprises to effectively utilize social media for business purposes.

The purpose of this paper is to evaluate green buildings from the angle of greenness and improve the evaluation system. And the matter-element extension method is used to evaluate the greenness of green buildings, in order to provide useful references for the evaluation system of green buildings.

First, this paper studies the aspects of safety and durability, health and comfort, living convenience, resource-saving, environmental liability and ecological quality, etc. For the first time, carbon emission is included in the evaluation system, 18 key evaluation indexes are determined by using the Delphi method, and the green building evaluation index system is established. Then, the combined weight method is proposed to determine the weight of each evaluation index, and the greenness evaluation model of green building is established with the matter-element extension method. Finally, taking Beijing Daxing International Airport as an example, the evaluation model of green building greenness was established to evaluate the building.

In this paper, the greenness evaluation model of green building established by the matter-element extension method solves the problem of incompatibility between qualitative and quantitative material elements in multi-factor evaluation. It makes the evaluation indexes more accurate and objective relative to the affiliation calculation of the evaluation set and improves the scientific, accuracy and reliability of the evaluation model.

In this paper, for the first time, carbon emission-related indicators are included in the green building evaluation system, which makes the evaluation system more perfect. In addition, a more scientific extension matter-element method is used to evaluate the greenness of green buildings, breaking the previous rough star evaluation method.

This paper generalises the GM(1,1) direct modeling method with a step by step majorizing grey derivative's whiten values to unequal time interval sequence modeling, and proves that the new method still has linear transformation consistency of the old method. The example indicates that the new method still has gradual approaching white exponential law coincidence property. With this new method, we then model the high precision soft foundation settlement.

This paper aims to investigate the equivalent relationship between accelerated corrosion tests and real environmental spectrum of suspenders in long-span suspension bridge considering multiple factors action.

Based on Faraday's law, corrosion current was used as a measure of metal corrosion, and the equivalent conversion relationship between laboratory environment and real service environment was established. The equivalent conversion method for bridge structural steel had been determined under different temperature, humidity, pH value and NaCl concentration conditions. The compilation of environmental spectra for large span bridges considering multiple factors and the principle of equivalent conversion have been proposed.

Environmental factors, including temperature, humidity, pH value and NaCl concentration, have significant impact on the corrosion degree of suspension steel wires, and only considering these two factors for equivalent conversion cannot accurately reflect the true service environment of the bridge. The 33.8-h salt spray accelerated corrosion test using the standard conditions can be equivalent to one year of suspenders corrosion in a real service environment.

The equivalent accelerated corrosion method for steel wires proposed in this study can effectively predict the corrosion degree of the suspenders, which has been verified to be correct and can provide theoretical guidance for the development of corrosion test plans for steel wires and engineering technical basis for anti-corrosion control and calendar life research of suspension bridge suspenders.

Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction for EIT is a nonlinear problem. A generalized inverse operator is usually ill-posed and ill-conditioned. Therefore, the solutions for EIT are not unique and highly sensitive to the measurement noise.

This paper develops a novel image reconstruction algorithm for EIT based on patch-based sparse representation. The sparsifying dictionary optimization and image reconstruction are performed alternately. Two patch-based sparsity, namely, square-patch sparsity and column-patch sparsity, are discussed and compared with the global sparsity.

Both simulation and experimental results indicate that the patch based sparsity method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages.

EIT image is reconstructed based on patch-based sparse representation. Square-patch sparsity and column-patch sparsity are proposed and compared. Sparse dictionary optimization and image reconstruction are performed alternately. The new method tolerates a relatively high level of noise in measured voltages.

The sharing economy has enjoyed rapid growth in recent years, and entered many traditional industries such as accommodation, transportation and lending. Although researchers in information systems and marketing have attempted to examine the impacts of the sharing economy on traditional businesses, they have not yet studied the rental housing market. Thus, this research aims to investigate the impact of the sharing economy (i.e. home-sharing) on traditional businesses (i.e. rental housing market).

The authors assemble rich data from multiple sources about the entry of a leading Chinese home-sharing platform (i.e. Xiaozhu.com) and local housing rental price index. Then, econometric models (i.e. linear panel-level data models) are employed for empirical investigation. Instrumental variables are used to account for potential endogeneity issues. Various robustness checks are adopted to establish the consistency of the findings.

Overall, the estimation results show that the entry of a home-sharing platform will decrease the local housing rental price. Moreover, this impact would be strengthened in a more developed city. Additionally, this impact would be strengthened with higher prices of new houses or second-hand houses.

First, this research is one of the first to study the impact of the sharing economy (i.e. home-sharing) on traditional markets (i.e. housing rentals). Second, it contributes to the relevant literature by documenting that the impact of a platform's entry is not uniform but contingent on city and housing market characteristics. Third, practically, the findings also offer important implications for platform operators and policy makers.

The purpose of this paper is to analyze raw materials, labor, capital, demand, related industries, strategies and policies influencing international competitiveness of Chinese textile and clothing industry.

The analysis is conducted using “Diamond Model”, in which raw materials, labor, capital, demand, related industries, strategies and policies are included as explanatory variables, and the impacts of international competitiveness on market share (MS), trade competitiveness(TC) and revealed comparative advantage(RCA) are examined based on the estimated coefficients of these variables.

These factors have different effects on TC, MS and RCA. While their effects on TC and MS are similar in sign even though their degree of significance differs, their effects on RCA are opposite to TC and MS except for capital. Raw materials and capital have negative effects on TC and MS, while the other factors have positive ones. Raw materials have positive effects on RCA, but all other factors have negative ones.

The results from this study imply that it is necessary to increase investment in fixed assets of Chinese textile and clothing industry, speed up the pace of upgrading equipment, improve the level of industrialization, while strengthening the supply of textile raw materials, and lowering raw material prices, thereby reducing the cost of textile and clothing enterprises.

To the best of the authors’ knowledge, this is the first empirical research made using econometric model about the impact of the main factors of trade competitiveness in Chinese textile and clothing industry based on the “Diamond Model”.

The aim of this study is to explore students' expectations and perceived effectiveness of computer-assisted review tools, and the differences in reliability and validity between human evaluation and automatic evaluation, to find a way to improve students' English writing ability.

Based on the expectancy disconfirmation theory (EDT) and Intelligent Computer-Assisted Language Learning (ICALL) theory, an experiment is conducted through the observation method, semistructured interview method and questionnaire survey method. In the experiment, respondents were asked to write and submit four essays on three online automated essay evaluation (AEE) systems in total, one essay every two weeks. Also, two teacher raters were invited to score the first and last papers of each student. The respondents' feedbacks were investigated to confirm the effectiveness of the AEE system; the evaluation results of the AEE systems and teachers were compared; descriptive statistics was used to analyze the experimental data.

The experiment revealed that the respondents held high expectations for the computer-assisted evaluation tools, and the effectiveness of computer scoring feedback on students was higher than that of teacher scoring feedback. Moreover, at the end of the writing project, the students' independent learning ability and English writing ability were significantly improved. Besides, there was a positive correlation between students' initial expectations of computer-assisted learning tools and the final evaluation of learning results.

The innovation lies in the use of observation methods, questionnaire survey methods, data analysis, and other methods for the experiment, and the combination of deep learning theory, EDT and descriptive statistics, which has particular reference value for future works.

The purpose of this paper is to present a novel method for minor fabric defects detection.

This paper proposes a PETM-CNN algorithm. PETM-CNN is designed based on self-similar estimation algorithm and Convolutional Neural Network. The PE (Patches Extractor) algorithm extracts patches that are possible to be defective patches to preprocess the fabric image. Then a TM-CNN (Triplet Metric CNN) method is designed to predict labels of the patches and the final label of the image. The TM-CNN can perform better than normal CNN.

This algorithm is superior to other algorithms on the data set of fabric images with minor defects. The proposed method achieves accurate classification of fabric images whether it has minor defects or not. The experimental results show that the approach is effective.

Traditional fabric defects detection is not effective as minor defects detection, so this paper develops a method of minor fabric images classification based on self-similar estimation and CNN. This paper offers the first investigation of minor fabric defects.

The purpose of this study is to design a spherical sensor which can detect the surge from various directions to lay the foundation of the research of surge.

This paper designed a spherical sensor to detect the impact force generated by the surge. To realize the depth and stability control of the shallow underwater vehicles, it is necessary to do research and analysis on the surge in shallow waters. The spherical sensor with novel structure was skillfully composed of 24 cantilever beam pressure-type strain sensors. It is powerful to detect the surge from various directions simultaneously.

It is proved that the spherical sensor can accurately collect the surge data from multiple directions through experiments, which laid the foundation of the anti-surge study.

Surge is not a new topic. But there is no effective tool to detect surge. The research of this paper is an attempt to provide an available tool to analyze surge. The research limitation is that the pool where the spherical sensor is installed is a little small. In the future, a bigger pool can be used.

A deep and comprehensive analysis of surge can be conducted according to the surge data detected by the spherical sensor to achieve the essential features of surge. This lays the foundation for the accurate control of Autonomous Underwater Vehicles (AUVs), especially fixed depth and stability control.

As the control accuracy of AUVs increases, the AUVs can perform much more difficult tasks such as port monitoring, underwater salvaging, underwater pipeline maintenance and so on. These can be applied in commercial applications or in the national defense of many countries.

A novel spherical sensor using 24 cantilever beam pressure-type strain sensors to detect the surge was designed. The spherical sensor was installed in the physical surge simulator to collect surge data and conduct an analysis of the collected data.