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To solve the problems of annotation noise, ambiguity recognition and nested entity recognition in the field of Chinese furniture, this paper aims to design a new recognition model ALE-BiLSTM-CRF.

This paper addresses the relative independence of text characters in the Chinese furniture domain named entity recognition (NER) task. It also considers the limited information provided by these text characters in this task. Therefore, a model named ALE-BiLSTM-CRF for Chinese furniture domain NER is proposed. First, the ERNIE pre-trained model is used to transform text into a dynamic vector that integrates contextual information. And adversarial learning is combined to generate adversarial samples to enhance the robustness of the model. Next, the BiLSTM module captures the temporal information of the context, and the multi-head attention mechanism integrates long-distance semantic features into the character vectors. Finally, a CRF layer is used to learn the constraints between labels, enabling the model to generate more reasonable and semantically consistent label sequences. This paper conducts comparative experiments with mainstream models on the Weibo data set, achieving an F1 score of 75.52%, demonstrating its generality and robustness. Additionally, comparative and ablation experiments are conducted on a self-constructed furniture data set in the Chinese furniture field, achieving an F1 score of 89.62%, verifying the model’s superiority and effectiveness.

This paper conducts comparative experiments with mainstream models on the Weibo data set, achieving an F1 score of 75.52%, demonstrating its generality and robustness. Additionally, comparative and ablation experiments are conducted on a self-constructed furniture data set in the Chinese furniture field, achieving an F1 score of 89.62%, verifying the model’s superiority and effectiveness.

This paper demonstrates its universality and generalization by conducting comparative experiments with mainstream models on the Weibo data set. It also conducts comparative experiments with representative pre-trained models on the furniture data set and conducts ablation experiments on the model itself, further demonstrating the superiority and effectiveness of the model.

In the furniture domain, NER aims to use various methods, including rule templates, machine learning and deep learning techniques, to extract structured information related to furniture from unstructured text. These pieces of information may include the name, material, brand, style and function of the furniture. By extracting and identifying these named entities, this paper can provide more accurate data support for furniture design, manufacturing and marketing, thereby promoting further development and innovation in the furniture industry.

In the furniture field, NER faces some special challenges, which are different from entity recognition in general fields. Furniture terminology is often highly specialized and complex in structure. At the same time, there may be a large number of nested entities in the text of the furniture field. For example, the furniture name “sofa bed” contains two entities “sofa” and “bed.” Current sequence labeling methods often find it difficult to recognize such nested entity structures simultaneously. Additionally, because furniture terminology and descriptions may change with trends and design styles, the model also needs to have a certain degree of adaptability and update capabilities. These reasons make it more difficult to extract information in the furniture field, and NER in the furniture field faces huge challenges.

This paper conducts comparative experiments with mainstream models on the Weibo data set, achieving an F1 score of 75.52%, demonstrating its generality and robustness. Additionally, comparative and ablation experiments are conducted on a self-constructed furniture data set in the Chinese furniture field, achieving an F1 score of 89.62%, verifying the model’s superiority and effectiveness.

By incorporating the defect feature information, an ML-based linkage between defects and fatigue life unaffected by the time scale is developed, the primary focus is to quantitatively assess and elucidate the impact of different defect features on fatigue life.

A machine learning (ML) framework is proposed to predict the fatigue life of LPBF-built Hastelloy X utilizing microstructural defects identified through nondestructive detection prior to fatigue testing. The proposed method combines nondestructive micro-computerized tomography (micro-CT) technique to comprehensively analyze the size, location, morphology and distribution of the defects.

In the test set, SVM-based fatigue life prediction exhibits the highest accuracy. Regarding the defect information, the defect size significantly affects fatigue life, and the diameter of the circumscribed sphere of the largest defect has a critical effect on fatigue life.

This comprehensive approach provides valuable insights into the fatigue mechanism of structural materials in defective states, offering a novel perspective for better understanding the influence of defects on fatigue performance.

The construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.

Based on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.

Results suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.

This study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.

The durability of concrete beams (150 × 250 × 1,600 mm³) strengthened using creative profiles with near-surface mounted (NSM) CFRP ropes under severe sulfate cyclic conditions was experimentally investigated.

Parabolic and straight profiles of NSM-CFRP ropes were implemented without and with internal vertical and lateral dowelings using the CFRP ropes. Another group of concrete beams was repaired using similar schemes after being exposed to sulfate damage. The mechanical response of virgin (control), and sulfate damaged beams as well as of those retrofitted before and after exposure to sulfates, was evaluated under a four-point loading setup.

Mechanical evaluation of concrete indicated an increase in concrete’s brittleness and a decrease in its compressive strength by 21%. The beams strengthened using proposed configurations before being exposed to severe sulfate attack showed reductions in load capacity, displacement ductility and toughness of up to 24, 22 and 53%, respectively, yet an increase in stiffness of up to 16% compared to counterpart virgin-strengthened beams. The implantation of vertical and lateral dowels helped maintain the load capacity and improve the stiffness and displacement ductility for former beams by as much as 35 and 16%, respectively. In contrast, beams exposed to the same stage of sulfate treatment and then repaired with a parabolic profile (with and without vertical dowels) attained or surpassed the mechanical performance of relevantly strengthened and then sulfate-treated ones. The adopted retrofitting configurations did not alter the ductile flexural failure mode observed for virgin and sulfate-damaged beams.

Concrete beams were strengthened and then subjected to sulfate attack in the absence of actual loading, which is not the case in the field.

Encouraging the use of the proposed repair configuration with CFRP ropes for concrete elements subjected to severe sulfate attack.

The repair proposed may help maintain the integrity of marine structures for a long time to save on municipal spending on repair issues.

Limited work (if none) on the evaluation of repair durability of concrete flexural elements under sulfate severe condition was reported.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

This paper examines the evolving landscape of accounting and finance research on the Chinese capital market, building on a previous study published at Abacus in 2018.

By incorporating data from 1999 to 2023, our analysis offers a detailed examination of shifts in academic focus, methodological advancements and thematic expansions over the last quarter-century.

The study reveals a substantial increase in accounting and finance publications related to the Chinese capital market in both Tier 1 and Asia-Pacific journals. The dynamic growth of the Chinese capital market during this period reflects profound economic transformations, characterized by technological innovations, sustainability commitments and regulatory reforms.

We conclude that the globally important Chinese capital market has attracted increasing academic attention, significantly advancing the understanding of accounting and finance research in China’s capital market.

As an important engine of economic growth, the digital economy is bringing new opportunities for the promotion of entrepreneurship. However, key questions regarding the extent of the effect of the digital economy on entrepreneurship remain unanswered. This study examines how the digital economy influences entrepreneurship in China using provincial data from 2011–2020, applying convergence tests and spatial econometric models.

Based on theoretical analysis and using macro provincial data covering the period of 2011–2020, we adopt a diversified empirical analytical method and apply a combination of the convergence trend test, spatial auto correlation test, and spatial Durbin model to test the research hypotheses.

First, there is spatial correlation between the digital economy and entrepreneurship. Second, the overall trend of China’s digital economy shows s convergence, with the whole country and the eastern region showing absolute β convergence and the whole country as well as the central and western regions showing β conditional convergence. Third, the digital economy can significantly promote entrepreneurship and has spatial spillover effects. Moreover, higher education has a negative moderating effect on the process of digital economy empowering entrepreneurship.

Studying the spatially correlated impacts of the digital economy on entrepreneurship enhances our understanding of its contribution to economic growth. Policy-makers can use these findings to develop targeted digital infrastructure investments in lagging provinces, guide entrepreneurs to better grasp the opportunities of the digital economy, and provide support for innovation and entrepreneurship. The findings also could offer Chinese experience that can be used to guide developing countries in utilizing the digital economy to enable entrepreneurship.

This paper expands and enriches the analytical focus on digital economy-empowered entrepreneurship and complements the current theoretical research on the moderating effect of the digital economy in empowering entrepreneurship.

The present study purports to unravel the mechanism in relationship among leadership behaviour integrity, organisational career development and employee engagement. Further, it also aims to understand if the employee feedback self-efficacy has any moderating influence on the relationship between leader behavioural integrity and organisational career development.

Pre-existing questionnaires are used for collecting data from a total of 417 employees working in the information technology industry operating within India. Analysis of the data is done using structural equation modelling technique.

Results of the study show that organisational career development partially mediates the relationship between leadership behavioural integrity and employee engagement. It is also found that feedback self-efficacy plays a moderating role in the relationship between leadership behavioural integrity and organisational career development.

The study helps to understand the mechanism of the relationship between leadership behavioural integrity and employee engagement through organisational career development with the support of ethical theory and social exchange theory. It also shows the moderating role played by feedback self-efficacy in the relationship between leadership behavioural integrity and organisational career development using social learning perspective.

Regulating and guiding the sound development of capital under a socialist system is not only a significant theoretical innovation rooted in Chinese practice by the Communist Party of China (CPC) but also a major practical exploration for the smooth progression of the party and the nation’s modernization endeavors.

Adopting an analytical framework that unifies theoretical, historical and practical logic, this paper explores the rationality of capital’s existence under a socialist system based on the notion that capital is a materialized production relationship. It also examines the staged evolution of capital theory under socialism, highlighting the great leap forward realized by the capital theory of socialism with Chinese characteristics.

With the CPC Central Committee’s deepening understanding of the capital patterns in the socialist system, capital-related policies have undergone a developmental journey through several stages – from the negation and elimination of capital to the support and operation of capital and finally to the stage of regulating and guiding capital. This reflects the reciprocal and complementary relationship between capital theory and capital policy.

In response to issues such as the disorderly expansion of capital in the new development stage, it is imperative to actively promote a practical pathway safeguarded by the adherence to Party leadership, guided by the capital theory of socialism with Chinese characteristics and targeted toward common prosperity.

Vertical cylindrical welded steel tanks are typical thin-walled structures that are very susceptible to buckling under settlement. The major concern in the design of these thin-walled structures is buckling failure. On this basis and by considering the findings of the previously reported research works, the stability performance of open-top steel tanks with various industrial applications under local support edge settlement is further investigated in this paper. This study aims to contribute to the current state-of-the-art in the design and retrofit of such thin-walled structures.

The buckling behaviors of numerous cylindrical shell models with various height-to-radius, radius-to-thickness and settlement span ratios are investigated through linear and nonlinear buckling analyses. The effects of addition of a top stiffening ring on the buckling behavior of cylindrical steel tanks are studied as well.

This parametric study demonstrates that the choice of the height-to-radius, radius-to-thickness and settlement span ratios as well as addition of the top stiffening ring can be quite effective on the stiffness and strength performances, deformations and stress distribution as well as intensity of vertical cylindrical welded steel tanks subjected to local support edge settlement.

This research endeavor was formulated on the basis of a comprehensive literature survey and demonstrates the relationship between geometrical as well as stiffening features and buckling stability performance of open-top tanks subjected to local support edge settlement and also provides practical recommendations for design and retrofit purposes.