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The purpose of this paper is to investigate the extent to which firms in the Sri Lankan apparel industry practice supply-chain-resilience (SCRes) capabilities and examine whether SCRes practices affect the performance and competitive advantage of those firms.

Uses a conceptual framework to assess SCRes capabilities and to investigate their impact on firm performance and competitive advantage. Uses partial least squares structural equation modeling (PLS-SEM) to quantitatively analyze questionnaire data collected from 89 Sri Lankan apparel manufacturers.

In the presence of SCRes capabilities in the apparel industry, this study finds that supply-chain risk-management culture positively affects SCRes capabilities, namely re-engineering, agility and collaboration. Agility shows the greatest influence on firm performance and competitive advantage.

This study is limited to the apparel industry sector (a manufacturing sector) in Sri Lanka to maintain the uniformity of the research constructs.

Results imply that management should pay more attention to enhancing SCRMC and prioritizing their SCRes capabilities.

This study is the first to assess SCRes capabilities in the apparel-manufacturing sector and examine the impact of SCRes capabilities on firm performance and competitive advantage.

The purpose of this paper is to focus on the influences of dynamic capability on breakthrough innovation and explores the essential mechanisms related to such influence. The impact of breakthrough innovation on organizational performance is also examined in conjunction with the manufacturing industry samples in China.

This paper follows an empirical design. Data are collected from a survey administered to manufacturing samples in China. Regression analysis is used to test the hypothesis.

Results show that the positive impact of breakthrough innovation on organization performance is verified. Technological breakthrough innovation affects both the organization’s financial performance and growth performance in a linear and positive way, while market breakthrough innovation affects organization performance positively with marginal increments.

This paper uses data of manufacturing industry of China, hence, an extended survey in depth and width is worthy of future investigation. In addition, more unique predisposing factors on breakthrough innovation should be considered in the context of China.

The research enriches the understanding of how the dynamic capability affects the enterprise’s breakthrough innovation, which helps entrepreneurs understand how to strategize to build dynamic capability during breakthrough innovation decisions and implementations.

Dynamic capability is uniquely viewed as a multi-dimensional concept from a perspective of modularity, while breakthrough innovation is composed of two constructs. It not only enriches the understanding of how dynamic capability affects breakthrough innovation but also discovers the non-linear impact of breakthrough innovation on enterprise’s organizational performance in the context of China’s manufacturing industry.

In the era of the digital economy, higher demands are placed on versatile talents, and the cultivation of students with innovative and entrepreneurial abilities has become an important issue for the further development of higher education, thus leading to extensive and in-depth research by many scholars. The study summarizes the characteristics and patterns of dual-innovation education at different stages of development, hoping to provide a systematic model for the development of dual-innovation education in China and make up for the shortcomings.

This paper uses Citespace software to visualize and analyze the relevant literature in CNKI and Web of Science databases from a bibliometric perspective, focusing on quantitative analysis in terms of article trends, topic clustering, keyword co-linear networks and topic time evolution, etc., to summarize and sort out the development of innovation and entrepreneurship education research at home and abroad.

The study found that the external characteristics of the literature published in the field of bi-innovation education in China and abroad are slightly different, mainly in that foreign publishers are more closely connected and have formed a more stable ecosystem. In terms of research hotspots, China is still in a critical period of reforming its curriculum and teaching model, and research on the integration of specialization and creative education is in full swing, while foreign countries focus more on the cultivation of students' entrepreneurial awareness and the enhancement of individual effectiveness. In terms of cutting-edge analysis, the main research directions in China are “creative education”, “new engineering”, “integration of industry and education” and “rural revitalization”.

Innovation and entrepreneurship education in China is still in its infancy, and most of the studies lack an overall overview and comparison of foreign studies. Based on the econometric analysis of domestic and foreign literature, this paper proposes a path for domestic innovation and entrepreneurship education reform that can make China's future education reform more effective.

The aim of this study is to first investigate the surface integrity of cylindrical rollers under grinding process and then design a reasonable superfinishing process that improve the anti-fatigue performance of cylindrical rollers by optimization of the surface integrity.

First, the white and dark layers produced by the grinding process is analyzed by microscope. Then, the influence of oilstone pressure on the stock removal, surface precision and crowned profile are explored. Finally, an optimal superfinishing process and a novel turnaround device are designed to improve surface integrity.

The experimental results show that as the oilstone pressure increases, the stock removal first increases and then remains stable. This hints that the stock removal of a single-time superfinishing process has an upper limit. In the current conditions, the maximum stock removal is 6 µm. Double-time superfinishing process and the turnover device can effectively eliminate the white and dark layers and improve the symmetric of roller profile. In addition, the surface precision is also improved.

The surface integrity of bearing rollers is very important to the application of industry field. The findings and the methods in the study can be helpful to improve the surface integrity of the bearing rollers.

The purpose of this paper is to propose a novel non-probabilistic reliability-based topology optimization (NRBTO) method for continuum structural design under interval uncertainties of load and material parameters based on the technology of 3D printing or additive manufacturing.

First, the uncertainty quantification analysis is accomplished by interval Taylor extension to determine boundary rules of concerned displacement responses. Based on the interval interference theory, a novel reliability index, named as the optimization feature distance, is then introduced to construct non-probabilistic reliability constraints. To circumvent convergence difficulties in solving large-scale variable optimization problems, the gradient-based method of moving asymptotes is also used, in which the sensitivity expressions of the present reliability measurements with respect to design variables are deduced by combination of the adjoint vector scheme and interval mathematics.

The main findings of this paper should lie in that new non-probabilistic reliability index, i.e. the optimization feature distance which is defined and further incorporated in continuum topology optimization issues. Besides, a novel concurrent design strategy under consideration of macro-micro integration is presented by using the developed RBTO methodology.

Uncertainty propagation analysis based on the interval Taylor extension method is conducted. Novel reliability index of the optimization feature distance is defined. Expressions of the adjoint vectors between interval bounds of displacement responses and the relative density are deduced. New NRBTO method subjected to continuum structures is developed and further solved by MMA algorithms.

Gas pipelines are facing serious risk because of the factors such as long service life, complex working condition and most importantly, corrosion. As one of the main failure reasons of gas pipeline, corrosion poses a great threat to its stable operation. Therefore, it is necessary to analyze the reliability of gas pipelines with corrosion defect. This paper uses the corresponding methods to predict the residual strength and residual life of pipelines.

In this paper, ASME-B31G revised criteria and finite element numerical analysis software are used to analyze the reliability of a special dangerous section of a gas gathering pipeline, and the failure pressure and stress concentration of the pipeline under three failure criteria are obtained. Furthermore, combined with the predicted corrosion rate of the pipeline, the residual service life of the pipeline is calculated.

This paper verifies the feasibility of ASME-B31G revised criteria and finite element numerical analysis methods for reliability analysis of gas pipelines with corrosion defect. According to the calculation results, the maximum safe internal pressure of the pipeline is 9.53 Mpa, and the residual life of the pipeline under the current operating pressure is 38.41 years, meeting the requirements of safe and reliable operation.

The analysis methods and analysis results provide reference basis for the reliability analysis of corroded pipelines, which is of great practical engineering value for the safe and stable operation of natural gas pipelines.

Using computer technology to realize ferrographic intelligent fault diagnosis technology is fundamental research to inspect the operation of mechanical equipment. This study aims to effectively improve the technology of deep learning technology in the field of ferrographic image recognition.

This paper proposes a binocular image classification model to solve ferrographic image classification problems.

This paper creatively proposes a binocular model (BesNet model). The model presents a more extreme situation. On the one hand, the model is almost unable to identify cutting wear particles. On the other hand, the model can achieve 100% accuracy in identifying Chunky and Nonferrous wear particles. The BesNet model is a bionic model of the human eye, and the used training image is a specially processed parallax image. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The work presented in this thesis is original, except as acknowledged in the text. The material has not been submitted, either in whole or in part, for a degree at this or any other university. The BesNet model developed in this article is a brand new system for ferrographic image recognition. The BesNet model adopts a method of imitating the eyes to view ferrography images, and its image processing method is also unique. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0150/

The purpose of this paper is to study the saturation and nonlinear performance of magnetic field in the air gap of switched reluctance motor (SRM).

The analytical method of sub-domain combined with the saturation compensation method is used to determine the nonlinear distribution of air gap magnetic field in SRM. Also, the resolutions of the two-dimensional (2D) Laplace’s equation and Poisson’s equation in polar coordinates are used to obtain the simplified expression of magnetic flux density.

For verifying the effectiveness of analytical model, the results are compared with those obtained from the 2D finite element method (FEM). The influence of magnetic saturation is taken into account by associating the sub-domain analysis result with the nonlinear B-H properties of stator and rotor iron. The magnetic flux density in radial and tangential direction considering the saturation effect may be calculated accurately. It can be seen that one can easily determine the linear analytical results accurately, whereas it is difficult to determine the magnetic flux density with saturation influence; especially at some local positions, there is a larger difference between analytical and FE model due to the complex boundary conditions.

This paper presents the development and optimization design of high-performance SRM.

The magnetic saturation may be taken into account for the SRM and analytical models support to simulated system performance.

The purpose of this paper is to focus on the operation strategy of high-performance alliance portfolios by analyzing the effect of alliance portfolios on the performance of focal firms, using post-structuralism of social network theory and contingency theory. In detail, this paper refines alliance portfolios into three dimensions, and studies the moderating role of context on the relation between alliance portfolios and firm performance.

The empirical study was carried out with second-hand data gathered from Internal Revenue Service. In total, this paper gathered data from 506 focal firms in Zhejiang Province from 2001 to 2010 as the sample to test the hypotheses.

Based on the empirical results, the authors find the positive effect of relational dimension (weak alliance portfolios) and partner dimension (the diversity of partners) on performance. The effect of the former will become weaker with the increasing environmental dynamic, while the effect of the latter will become stronger. However, the structural dimension (alliance portfolios size) and relational dimension (new partners) have the negative effect on performance. And the negative effect will become stronger under high environmental dynamic. Moreover, the negative effect of non-local partners on performance becomes stronger when the environmental dynamic is high.

The paper reveals that with the industry transformation caused by “internet +,” companies have been required go beyond traditional dyadic alliance management perspective. That is to say, individual alliance relationship should be seen as a part of a much broader picture of alliance portfolio. As such, the framework may help companies to manage their alliance portfolios by matching high-performance alliance portfolios to the external environment to produce a synergistic effect (Lea et al., 2006; Tritos et al., 2013; Keith et al., 2014) taking the characteristics of the configuration of alliance portfolios into consideration.

The paper presents a model that explains the effect of three dimensions of alliance portfolios on the performance of focal firms in different contexts through empirical study. This paper also integrates post-structuralism of social network theory and contingency theory to enable the understanding on the configuration of alliance portfolios.

The possibility of using a pattern recognition system for wear particle analysis without the need of a human expert holds great promise in the condition monitoring industry. Auto-segmentation of their images is a key to effective on-line monitoring system. Therefore, an unsupervised segmentation algorithm is required. The purpose of this paper is to present a novel approach based on a local color-texture feature. An algorithm is specially designed for segmentation of wear particles’ thin section images.

The wear particles were generated by three kinds of tribo-tests. Pin-on-disk test and pin-on-plate test were done to generate sliding wear particles, including severe sliding ones; four-ball test was done to generate fatigue particles. Then an algorithm base on local texture property is raised, it includes two steps, first, color quantization reduces the total quantity of the colors without missing too much of the detail; second, edge image is calculated and by using a region grow technique, the image can be divided into different regions. Parameters are tested, and a criterion is designed to judge the performances.

Parameters have been tested; the scale chosen has significant influence on edge image calculation and seeds generation. Different size of windows should be applied to varies particles. Compared with traditional thresholding method along with edge detector, the proposed algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the present method is suited for wear particles’ image segmentation and can be put into practical use in wear particles’ identification system.

One major problem is when small particles with similar texture are attached, the algorithm will not take them as two but as one big particle. The other problem is when dealing with thin particles, mainly abrasive particles, the algorithm usually takes it as a single line instead of an area. These problems might be solved by introducing a smaller scale of 9 × 9 window or by making use of some edge enhance technique. In this way, the subtle edges between small particles or thin particles might be detected. But the effectiveness of a scale this small shall be tested. One can also magnify the original picture to double or even triple its size, but it will dramatically increase the calculating time.

A new unsupervised segmentation algorithm is proposed. Using the property of the edge image, we can get target out of its background, automatically. A rather complete research is done. The method is not only introduced but also completely tested. The authors examined parameters and found the best set of parameters for different kinds of wear particles. To ensure that the proposed method can work on images under different condition, three kinds of tribology tests have been carried out to simulate different wears. A criterion is designed so that the performances can be compared quantitatively which is quite valuable.