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Traditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations.

The authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings.

The authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG.

It requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge.

The embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences.

The proposed method is a practical embedding-driven model that accurately captures diverse user preferences.

Book sorting system is one of specific application in smart library scenarios, and it now has been widely used in most libraries based on RFID (radio-frequency identification devices) technology. Book identification processing is one of the core parts of a book sorting system, and the efficiency and accuracy of book identification are extremely critical to all libraries. In this paper, the authors propose a new image recognition method to identify books in libraries based on barcode decoding together with deep learning optical character recognition (OCR) and describe its application in library book identification processing.

The identification process relies on recognition of the images or videos of the book cover moving on a conveyor belt. Barcode is printed on or attached to the surface of each book. Deep learning OCR program is applied to improve the accuracy of recognition, especially when the barcode is blurred or faded. The approach the authors proposed is robust with high accuracy and good performance, even though input pictures are not in high resolution and the book covers are not always vertical.

The proposed method with deep learning OCR achieves best accuracy in different vertical, skewed and blurred image conditions.

Methods that the authors proposed need to cooperate and practice in different book sorting machine.

The authors collected more than 500 books from a library. These photos display the cover of more than 100 randomly picked books with backgrounds in different colors, each of which has about five different pictures captured from variety angles. The proposed method combines traditional barcode identification algorithm with the authors’ modification to locate and deskew the image. And deep learning OCR is involved to enhance the accuracy when the barcode is blurred or partly faded. Book sorting system design based on this method will also be introduced.

Experiment demonstrates that the accuracy of the proposed method is high in real-time test and achieves good accuracy even when the barcode is blurred. Deep learning is very effective in analyzing image content, and a corresponding series of methods have been formed in video content understanding, which can be a greater advantage and play a role in the application scene of intelligent library.

We obtained information on China Gerui from secondary published sources, including annual reports downloaded from the Securities and Exchange Commission’s (SEC) EDGAR database, news sites and newspapers, the company’s website and journal articles. One of the authors visited the China Gerui plant in Henan, China.

China Gerui, a Chinese metal fabrication company, enjoyed exponential growth because of its location, product innovation and ability to move up the value chain. At the height of its success, the company listed on the Nasdaq and had plans to raise capital to fund ambitious expansion plans. Unfortunately, four years after listing on Nasdaq, the company received a letter from the listing qualifications department notifying China Gerui that they were not in compliance with Nasdaq’s filing requirements because it had not filed its Form 20-F. Now, the company had only five days to decide whether to request an appeal of the letter.

This case is best suited for higher-level undergraduate accounting and finance courses such as intermediate accounting, auditing, international accounting, financial statement analysis, corporate finance and investments analysis. It is especially appropriate for graduate-level global accounting and advanced financial statement analysis courses. In these courses, the best placement is after coverage of SEC regulations and requirements for financial statement reporting and disclosure. Moreover, the case may be used as a tool to demonstrate the step-by-step process for searching and retrieving information from a public company’s filings through the SEC’s EDGAR database.

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Project complexity is a critical issue that has increasingly attracted attention in both academic and practical circles. However, there are still many gaps in the research on project complexity, such as the differentiated conceptualization of complexity and disjointed operationalization in the measurements. Therefore, this paper aims to conduct a systematic and detailed literature review on the concept, dimensions, assessment, and underlying mechanisms of project complexity.

A systematic literature review methodology was applied to search and synthesize the research on project complexity, and a final sample of 74 journal articles was identified.

This study first summarizes the concepts of project complexity from three different theoretical perspectives, and then identifies different approaches of measurement, evaluation, or simulation to assess project complexity. This paper finally establishes an integrative framework to synthesize the antecedents, mediators and moderators, and outcomes of project complexity, generating four suggestions for future research.

This study summarizes the definition and operationalization of project complexity to reduce the discrepancies in the existing research and offers an integrative framework to offer a broad overview of the current understanding of project complexity, providing a potential way forward for addressing project complexity.

Internet of Things (IoT), a strategic emerging industry, has brought a new driving force to the global economic growth, as well as an effective solution to break the barrier of economic development. However, standards system of IoT is not yet mature, existing obvious overlapping and even conflicting standards, and enterprises lack mature technology standardization model, which severely restricts the structural upgrading and development of IoT. The paper aims to discuss these issues.

In this regard, this study combs the research context of the IoT, technology standardization and competition behavior, and analyze technology standardization models of IoT by combining theory with practice. Using game theory and profit function, this paper analyzes the selection mechanism of standardization model of IoT enterprises, and explores practical application of these models using competitive behavior theory.

First, in the process of standardization, technology standardization model n enterprises is not single, nor is it immutable. Second, the trend of internationalization of technical standards is becoming more and more obvious. Third, if LoT enterprises want to achieve their own technology standardization, the corresponding competitive behavior is essential. Fourth, with the change of innovation capability and market forces, the competitive behavior of enterprises should be improved accordingly to better adapt to the changes of internal and external environment and ensure the realization of the standardization of enterprises. Fifth, if IoT attempts to achieve the same globalization as the internet, there must be a set of support systems.

Finally, some suggestions are given for the future development in the field of IoT. This study will provide some theoretical support for promoting the development of standards and enhancing the competitiveness of IoT enterprises.

This paper aims to improve the kinematic modeling accuracy of a spatial three-degrees-of-freedom compliant micro-motion parallel mechanism by proposing a modified modeling method based on the structural matrix method (SMM).

This paper analyzes the problem that the torsional compliance equation of the circular notched hinge is no longer applicable because it is subject to bilateral restrained torsion. The torsional compliance equation is modified by introducing the relative length coefficient. The input coupling effect, which is often neglected, is considered in kinematic modeling. The symbolic expression of the input coupling matrix is obtained. Theory, simulation and experimentation are presented to show the validity of the proposed kinematic model.

The results show that the proposed kinematics model can improve the modeling accuracy by comparing the theoretical, finite element method (FEM) and experimental method.

This work provides a feasible scheme for CMPM kinematics modeling. It can be better applied to the optimization design based on the kinematic model in the future.

The purpose of this paper is to use an index decomposition analysis to investigate the driving forces of China’s CO₂ emissions related to fixed asset investments from 2003 to 2015.

This paper uses an index decomposition analysis to investigate the driving forces of China’s CO₂ emissions related to fixed asset investments from 2003 to 2015. To make policy recommendations, this paper identifies three effects. An approach to calculating energy-relevant CO₂ emissions is also presented.

The results suggest that the amount of CO₂ emissions related to fixed asset investments increased during the entire period. The social and economic effect played a major role in promoting carbon emissions, followed by the fixed asset effect. Therefore, the activity factor was the dominant positive factor, followed by the construction factor. The negative element was the energy effect, in which the energy intensity factor played an important role in reducing emissions, followed by the structural factor. Moreover, the carbon intensity factor might be a potential inhibitory force in reducing carbon emissions.

A steady financial policy, relaxed family planning, sustainable urbanization, strategy of innovation-driven development, reform of scientific and technological structures, development of science and technology and exploration of new energy sources are proposed to mitigate carbon emissions from fixed asset investments. The conclusion also provides a reference for developing countries in similar situations.

This paper uses an index decomposition analysis to investigate the driving forces of China’s CO₂ emissions related to fixed asset investments from 2003 to 2015. To make policy recommendations, this paper identifies three effects. An approach to calculating energy-relevant CO₂ emissions is also presented.

The performance of the first batch of listed companies since the restart of new initial public offerings (IPOs) in January 2014 and their accounting information face repeated and volatile questioning from different sides. This paper aims to take Guirenniao (China) Co. Ltd. (GRN for short), one of the first batch of listed companies in 2014 that suffered performance decline, as an example to analyze how it managed earnings before IPO.

This paper examines earnings management signs that exist in GRN through analysis of its financial statements compared to those of its industry peers. This paper then uses the modified Jones model to detect its accrual earnings management and build three models, which are abnormal levels of cash flows from operations, abnormal production costs and abnormal discretionary expenses, to detect real earnings management.

This paper finds that GRN managed earnings through accrual and real activities in 2012 and 2013. Finally, this paper provides evidence on the specific methods of earnings management, which are easing credit policy to recognize revenue in advance, abnormal expansion, decreasing costs and connected transactions.

This paper examines earnings management signs exist in GRN through analysis of its financial statements comparing to those of its industry peers. This paper then uses the modified Jones Model to detect its accrual earnings management and build three models which are abnormal levels of cash flows from operations, abnormal production costs and abnormal discretionary expenses to detect real earnings management.

This study aims to investigate whether gender diversity in audit committees affects the purchase of nonaudit services in China. Results from family and nonfamily firms are compared and the critical mass participation of females are further examined.

The sample comprises 1,834 Chinese listed companies from 2012 to 2021, among which 910 are family firms. The Heckman (1979) two-stage model is used to mitigate the potential endogeneity issue in the selection of gender diversity. Propensity score matching is also used to further alleviate the endogeneity problem in relation to family firms.

Results show a significant and negative correlation between the gender diversity in audit committees and nonaudit service fees. This association is more apparent in nonfamily than in family firms. Findings are consistent and robust to endogeneity tests and sensitivity analyses. The analysis of critical mass and symbolic participation shows that three female directors can more significantly restrain nonaudit fees than one to two females on the board.

This study contributes to literature on resource dependence theory, which posits that audit committees help enterprises establish contact with auditors, improve the company legitimacy, assist in communication and provide relevant expertise. This study also relates to agency theory, which holds that differences in the severity of types I and II agency problems between family and nonfamily firms lead to differences in auditor selection and related costs.

Extending from previous research on the relation between the gender diversity in audit committees and nonaudit fees, the present study delves into this connection within the context of China, an emerging economy. As a result, this investigation offers novel insights and expands upon current knowledge. In addition, the correlation between the gender diversity of audit committees and nonaudit fees is explored for family and nonfamily firms.

This study explores the influence of the coronavirus disease 2019 (COVID-19) career experience on the investment behavior and risk tolerance of chief executive officers (CEOs). Specifically, this study focuses on CEOs' abilities to allocate financial assets and maintain solvency.

This study adopts a comprehensive approach to analyze financial assets and asset-to-liability ratios. Financial data and individual information of CEOs from listed companies are collected from 2020Q1 to 2021Q4, along with statistics on confirmed COVID-19 cases. Instrumental and alternative variables are used to examine the robustness and endogeneity of the research, ensuring a thorough analysis.

A significant positive correlation is revealed between CEOs' COVID-19 career experience and their capacity to effectively allocate financial assets. However, COVID-19 has a negative effect on firm performance in terms of solvency. These findings contribute to the empirical evidence linking the pandemic to company performance, representing part of the initial research in this area.

The study suggests that the implementation of potential policy implications, such as loose monetary policies and tax and fee reduction measures, may alleviate the tax burden on listed companies.