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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.

Fabric defects detection is vital in the automation of textile industry. The purpose of this paper is to develop and implement a new fabric defects detection method based on adaptive wavelet.

Fabric defects can be regarded as the abrupt features of textile images with uniform background textures. Wavelets have compact support and can represent these textures. When there is an abrupt feature existed, the response is totally different with the response of the background textures, so wavelets can detect these abrupt features. This method designs the appropriate wavelet bases for different fabric images adaptively. The defects can be detected accurately.

The proposed method achieves accurate detection of fabric defects. The experimental results suggest that the approach is effective.

This paper develops an appropriate method to design wavelet filter coefficients for detecting fabric defects, which is called adaptive wavelet. And it is helpful to realize the automation of textile industry.

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

The method based on joint low-rank and spare matrix recovery, since patterned fabric is manufactured by a set of predefined symmetry rules, and it can be seen as the superposition of sparse defective regions and low-rank defect-free regions. A robust principal component analysis model with a noise term is designed to handle fabric images with diverse patterns robustly. The authors also estimate a defect prior and use it to guide the matrix recovery process for accurate extraction of various fabric defects.

Experiments on plain and twill, dot-, box- and star-patterned fabric images with various defects demonstrate that the method is more efficient and robust than previous methods.

The authors present a RPCA-based model for fabric defects detection, and show how to incorporate defect prior to improve the detection results. The authors also show that more robust detection and less running time can be obtained by introducing a noise term into the model.

Automatic slub detection is vital in the classification and identification of fabric images. This paper seeks to present a rapid and accurate approach for automatic detection of slub in fabric images using Gabor filters.

Slub can be regarded as defects along weft or warp. Gabor filters as bandpass filters consider the directional characteristics of slub and its frequency spectrum after Fourier transform. Choosing appropriate parameters for Gabor filters, slub can be detected accurately.

The proposed method achieves automatic detection of slub. The experimental results suggest that the authors approach is effective.

This paper considers appropriate parameters to design a Gabor filter for automatic detection of slub. And it is helpful to classify and identify fabric images.

The effectiveness of rhetorical structure is essential to communicate key messages in research articles (RAs). The interdisciplinary nature of library and information science (LIS) has led to unclear patterns and practice of using rhetorical structures. Understanding how RAs are constructed in LIS to facilitate effective scholarly communication is important. Numerous studies investigated the rhetorical structure of RAs in a range of disciplines, but LIS articles have not been well studied.

In this study, the authors encoded rhetorical structures to 2,216 articles in the Journal of the Association for Information Science and Technology covering a period from 2001 to 2018 with the approaches of co-word analysis and visualization. The results show that the predominant rhetorical structures used by LIS researchers follow the sequence of Introduction-Literature Review-Methodology-Result-Discussion-Conclusion (ILMRDC).

The authors' temporal examination reveals the shifts of evolutionary pattern of rhetorical structure in 2008 and 2014. More importantly, the authors' study demonstrates that rhetorical structures have varied greatly across research areas in LIS community. For example, scholarly communication and scientometrics studies tend to exclude literature review in articles.

The present paper offers a first systematic examination of how rhetorical structures are used in a representative sample of a LIS journal, especially from a temporal perspective.

The purpose of this paper is to propose a new group decision‐making approach, which can only use simple mathematical calculations to perform a group decision‐making task.

The large column and large row (LCLR) method is designed and applied.

The paper finds four propositions to support LCLR methods to be a simple and effectual means for group decision making.

The aggregated matrix constructed in LCLR methods should be generated from non‐contradictory circles.

Effective group decision‐making results can be obtained by easily used methods, not necessarily by using complex mathematics technology to conduct the task.

The new approach based on LCLR methods proposed in this paper may be one of the most easily used and effective means for group decision making.

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.

This study aims to further refine the model, explore the influence of cutting parameters on the machining process, and apply it to practical engineering to improve the efficiency and quality of titanium alloy machining.

This paper establishes a comprehensive thermo-mechanical fully coupled orthogonal cutting model. This paper aims to couple the modified Johnson–Cook constitutive model, damage model and contact model to construct a two-dimensional orthogonal cutting thermo-mechanical coupling model for high-speed cutting of Ti6Al4V. The model considers the evolution of microstructures such as plastic deformation, grain dislocation rearrangement, dynamic recrystallization, as well as stress softening and hardening occurring continuously in Ti6Al4V metal during high-speed cutting. Additionally, the model incorporates friction and contact between the tool and the workpiece. It can be used to predict parameters such as cutting process, cutting force, temperature distribution, stress and strain in titanium alloy machining. The study establishes the model and implements corresponding functions by writing Abaqus VUMAT and VFRICTION subroutines.

The use of different material constitutive models can significantly impact the prediction of the cutting process. Some models may more accurately describe the mechanical behavior of the material, thus providing more reliable prediction results, while other models may exhibit larger deviations. Compared to the Tanh model, the proposed model achieves a maximum improvement of 8.9% in the prediction of cutting force and a maximum improvement of 20.9% in the prediction of chip morphology parameters. Compared to experiments, the proposed model achieves a minimum prediction error of 2.8% for average cutting force and a minimum error of 0.57% for sawtooth parameters. This study provides a comprehensive theoretical foundation and practical guidance for orthogonal cutting of titanium alloys. The model not only helps engineers and researchers better understand various phenomena in the cutting process but also serves as an important reference for optimizing cutting processes.

The originality of this research is guaranteed, as it has not been previously published in any journal or publication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0168/

Prior studies have consistently shown that leader psychological capital is beneficial for leader–member exchange (LMX) and followers’ outcomes. In this study, the authors challenge this consensus; they propose that a leader with high-level psychological capital may decrease LMX and promote followers’ turnover intention when encountering a follower with low-level psychological capital. Only congruent psychological capital in leader–follower dyads increases LMX and decreases turnover intention.

A two-wave survey was designed to collect data from a sample of 207 leader–follower dyads in the service industries of China. Polynomial regression combined with the response surface analysis was used to test the hypotheses.

(1) LMX increased when the levels of psychological capital between leaders and followers were congruent, but LMX suffered when they were not congruent (e.g. leaders’ psychological capital was higher than followers’ or otherwise); (2) in the conditions of psychological capital congruence, LMX was higher when a leader’s and a follower’s psychological capital were both high than low; (3) LMX mediated the relationship between psychological capital congruence and followers’ turnover intention.

These findings provide a novel perspective on understanding of the function of psychological capital and its implications for turnover management.

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.