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In the fabric manufacturing industry, various unfavorable factors, including machine fault and yarn breakage, can easily cause fabric defects and affect product quality, begetting huge economic losses to enterprises. Thus, automatic fabric defect detection systems have become an important development direction. Herein, the most common defects in the fabric production process, like ribbon yarn, broken yarn, cotton ball, holes, yarn shedding and stains, are detected. Current fabric defect detection systems afford low detection accuracy and a high missed detection rate for small target fabric defects. Therefore, this study proposes deep learning technology for automatically detecting fabric defects by improving the YOLOv5s target detection algorithm. The improved algorithm is termed YOLOv5s-4SCK, which can effectively detect fabric defects. This study aims to discuss the aforementioned issues.

Specifically, based on the YOLOv5s algorithm, first, the structure of YOLOv5s is modified to add a small target detection layer, fully utilize deep and shallow features and reduce the missed detection rate of small target fabric defects. Second, the integration of CARAFE upsampling enables the effective retention of feature information and maintenance of a certain computational efficiency, thereby improving the detection accuracy. Finally, the K-Means++ clustering algorithm is used to analyze the position of the center point of the prior box to better obtain the anchor box and improve the average accuracy and evaluation index of detection.

The research results show that the YOLOv5s-4SCK algorithm increases the accuracy by 4.1% and the detection speed by 2 f.s-1 compared to the original YOLOv5s algorithm, and it effectively improves the original YOLOv5s problem of high missed detection rate of small targets.

The YOLOv5s-4SCK proposed in this paper can effectively reduce the missed detection rate of fabric defects, improve the detection efficiency and has certain industrial value.

The proposed algorithm can quickly identify fabric defects, effectively improving the detection rate. In the future, the proposed algorithm will be applied in the actual industry.

Automatic fabric defect detection reduces the manpower of inspectors, and the proposed YOLOv5s-4SCK algorithm is also suitable for other recognition fields.

The proposed YOLOv5s-4SCK algorithm has been tested using real cloth to ensure its accuracy, and its performance is better than the original YOLOv5s algorithm.

A serious global public health emergency (GPHE) like the COVID-19 aggravates the inequilibrium of medical care and other critical resources between wealthy and poor nations, which, coupled with the collision of cultures, indicates the vital need for developing humanitarian knowledge transcending cultures. Given the scarcity of literature addressing such unprecedent issues, this paper thus proposes new, unconventional viewpoints and future themes at the intersection of knowledge management (KM) and humanitarian inquiry.

This paper is conceptual in nature. The data of the World Bank and the Office for the Coordination of Humanitarian Affairs are analysed to introduce some emerging real impact topics regarding cross-cultural conflicts and humanitarian knowledge in the post-COVID business world. The theoretical foundation was built upon a critical literature review.

This paper synthesizes the perspectives of culture, KM and the humanistic philosophy to distil the core component of cultural intelligence and comparatively and thereby illuminating why cross-cultural metacognition acts as a priori for achieving cosmopolitan humanitarian knowledge.

This paper provides profound implications to academics by highlighting the importance to formulating new, inter-disciplinary themes or unorthodox, phenomenon-driven assumptions beyond the traditional KM domain. This paper also offers practitioners and policymakers valuable insights into coping with the growing disparity between high- and low-income countries by showing warning signs of a looming humanitarian crisis associated with a GPHE context.

This paper does not aim to claim the birth of a new domain but call for more research on developing a normative theory of humanitarian knowledge as transcendence of cultures. It implies uncharted territories of great interest and potential for the real impact KM community.

Suggests that misunderstandings frequently occur when trying to understand Chinese language and culture, and so gives the implied meaning of various Chinese expressions and sayings such as greetings, thanks, respect, age, congratulations and taboo subjects.

Cross-cultural cognitive paradoxes have frequently broken the existing boundaries of knowledge and stimulated demands for knowledge creation (KC), and such paradoxes have triggered and will continue to trigger novel risks in the context of international business (IB). Given the nascency of relevant issues, this study aims to develop a more comprehensive understanding of KC across cultures by proposing a Yin-Yang dialectical systems theory of KC as micro-foundation to more systematically frame the risk/paradox-resolving mechanism elicited by cultural collisions.

This paper is conceptual in nature. The authors first critically review the literature to lay a broad theoretical foundation. Integrating the philosophy- and praxis-based views, the authors reposition knowledge as a Yin-Yang dialectical system of knowing, with yin representing the tacit while yang represents the explicit. Next, the authors justify the underling logic of realising KC through a contradiction-resolving process. On this basis, the authors draw upon the Yijing’s Later Heaven Sequence (LHS) as the source domain of a heuristic metaphor to reconceptualise KC as a dynamic capability in the IB context.

Using the LHS paradigm to metaphorically map the intricate patterns of interaction and interconnectivity among the involved individuals, organisations and all related stakeholders, this research identifies and theorises the overall dynamic capability of KC in the IB context, which comprises five sets of processes: contradiction, conflict, communication, compromise and conversion.

This research highlights that KC is simultaneously activated and constrained by human actions as well as by the socially constructed context in which it emerges, which helps individuals, organisations and policy makers more clearly frame the novel risks induced by cross-cultural cognitive conflicts in the IB context.

The authors synthesise Yin-Yang dialectics with the approach of collective phronesis, proposing a novel, praxis-oriented Yin-Yang dialectical systems theory of KC. It provides a deeper understanding of the epistemological paradox inherent in all knowledge, thus enabling KC to be rationalised by a sounder logical reasoning. By fusing the macro and micro perspectives on KC, the authors also enrich existing theory and future theory building in the domain of knowledge management.

The purpose of this paper is to rank the users’ attitude on major components of mobile cloud computing (MCC) security and investigate the degree of impact of these components on MCC security as a whole.

Hypotheses were evolved and tested by data collected through an online survey-questionnaire. The survey was administered to 363 users from Chinese organizations. Statistical analysis was carried out and structural equation model was built to validate the interactions.

The eight components of MCC security in the order of importance are as follows: mobile device related, user identity related, deployment model related, application-level security issues, data related, virtualization related, network related and service delivery related. The empirical analysis validates that these security issues are having significant impact on perceived security of MCC.

Constant vigilance on these eight issues and improving the level of user awareness on these issues enhance the overall security.

These issues can be used for designing and developing secured MCC system.

While several previous research has studied various security factors in the MCC security domain, a consolidated understanding on the different components of MCC security is missing. This empirical research has identified and ranked the major components of MCC security. The degree of impact of each of these components on overall MCC security is identified. This provides a different perspective for managing MCC security by explaining what components are most important.

During the construction process of the China Railway Track System (CRTS) I type filling layer, the nonwoven fabric bags have been used as grouting templates for cement asphalt (CA) emulsified mortar. The porous structure of nonwoven fabrics endowed the templates with breathability and water permeability. The standard requires that the volume expansion rate of CA mortar must be controlled within 1%–3%, which can generate expansion pressure to ensure that the cavities under track slabs are filled fully. However, the expansion pressure caused some of the water to seep out from the periphery of the filling bag, and it would affect the actual mix proportion of CA mortar. The differences in physical and mechanical properties between the CA mortar under track slabs and the CA mortar formed in the laboratory were studied in this paper. The relevant results could provide important methods for the research of filling layer materials for CRTS I type and other types of ballastless tracks in China.

During the inspection of filling layer, the samples of CA mortar from different working conditions and raw materials were taken by uncovering the track slabs and drilling cores. The physical and mechanical properties of CA mortar under the filling layer of the slab were systematically analyzed by testing the electrical flux, compressive strength and density of mortar in different parts of the filling layer.

In this paper, the electric flux, the physical properties and mechanical properties of different parts of CA mortar under the track slab were investigated. The results showed that the density, electric flux and compressive strength of CA mortar were affected by the composition of raw materials for dry powders and different parts of the filling layer. In addition, the electrical flux of CA mortar gradually decreased within 90 days’ age. The electrical flux of samples with the thickness of 54 mm was lower than 500 C. Therefore, the impermeability and durability of CA mortar could be improved by increasing the thickness of filling layer. Besides, the results showed that the compressive strength of CA mortar increased, while the density and electric flux decreased gradually, with the prolongation of hardening time.

During 90 days' age, the electrical flux of the CA mortar gradually decreased with the increase of specimen thickness and the electrical flux of the specimens with the thickness of 54 mm was lower than 500 C. The impermeability and durability of the CA mortar could be improved by increasing the thickness of filling layer. The proposed method can provide reference for the further development and improvement of CRTS I and CRTS II type ballastless track in China.

The construction of cement asphalt (CA) emulsified mortar can obviously disturb the slab status after the fine adjustment. To decrease or eliminate the influence of CA mortar grouting on track slab geometry status, the effects of grouting funnel, slab pressing method, mortar expansion ratio, seepage ratio and grouting area on China Railway Track System Type (CRTS I) track slab geometry status were discussed in this paper.

Combined with engineering practice, this paper studied the expansion law of filling layer mortar, the liquid level height of the filling funnel, the pressure plate device and the amount of exudation water and systematically analyzed the influence of filling layer mortar construction on the state of track slab. Relevant precautions and countermeasures were put forward.

The results showed that the track slab floating values of four corners were different with the CA mortar grouting and the track slab corner near CA mortar grouting hole had the maximum floating values. The anti-floating effect of “7” shaped slab pressing device was more efficient than fixed-joint angle iron, and the slab floating value could be further decreased by increasing the amount of “7” shaped slab pressing devices. After CA mortar grouting, the track slab floating pattern had a close correlation with the expansion rate and water seepage rate of CA mortar over time and the expansion and water seepage rate of the mortar were faster when the temperature was high. Furthermore, the use of strip CA mortar filling under the rail bearing platform on both sides could effectively reduce the float under the track slab, and it could also save mortar consumption and reduce costs.

This study plays an important role in controlling the floating values, CA mortar dosage and the building cost of projects by grouting CA mortar at two flanks of filling space. The research results have guiding significance for the design and construction of China's CRTS I, CRTS II and CRTS III track slab.

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

This study aims to examine an important mechanism in the policy-led institutional transitions in China, namely, Te Shi Te Ban (Special Treatments for Special Matters) – an institutional device that facilitates policy implementation. The discussions are contextualized based on the latest chapter of China’s institutional transition, known as the reform initiative of Fang Guan Fu (i.e. the FGF reform: delegate power, streamline administration and optimize government services), which is a policy regime introduced in 2018 to improve the state-market relationship for better socioeconomic development.

Based on the theoretical lens of proto-institutions and institutional work and using real-life examples from mass media, this perspective paper examines the effects of the Special Treatments in the institutional transition under the FGF Reform.

The Special Treatments are the proto-institutions purposively adopted by the regulators in China to innovate, supervise and renovate the rules and norms during policy implementation. They produce both incremental and radical institutional effects which allow for a more efficient and effective policy-led institutional transition.

This study contributes to institutional theory in the Chinese management context. Foremost, this study introduces the concept of proto-institutional work and shows how proto-institutions can serve as a mechanism to support and manage the process of institutional transition. In addition, to the best of the authors’ knowledge, this paper is the first to study the FGF Reform – the latest reform initiative in China and theorize an under-researched but important mechanism in its institutional environment – the Special Treatments for Special Matters.

In the early days of the COVID-19 pandemic, an area could only report its first positive cases if the infection had spread into the area and if the infection was subsequently detected. A standard probit model does not correctly account for these two distinct latent processes but assumes there is a single underlying process for an observed outcome. A similar issue confounds research on other binary outcomes such as corporate wrongdoing, acquisitions, hiring, and new venture establishments. The bivariate probit model enables empirical analysis of two distinct latent binary processes that jointly produce a single observed binary outcome. One common challenge of applying the bivariate probit model is that it may not converge, especially with smaller sample sizes. We use Monte Carlo simulations to give guidance on the sample characteristics needed to accurately estimate a bivariate probit model. We then demonstrate the use of the bivariate probit to model infection and detection as two distinct processes behind county-level COVID-19 reports in the United States. Finally, we discuss several organizational outcomes that strategy scholars might analyze using the bivariate probit model in future research.