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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.

This study aims to expand the reliability and special functions of lightweight materials for high-end equipment and green manufacturing, so that it is the first such research to carry out nano-composite technology of nickel-coated carbon nanotubes (Ni-CNTs)-based titanium-zirconium chemical conversion on aluminum alloy substrate.

Corrosion behavior of various coatings was investigated using dropping corrosion test, linear polarization and electrochemical impedance spectroscopy. The results showed that the corrosion resistance of the nano-composite conversion coatings was significantly improved to compare with the conventional titanium-zirconium conversion coating. The morphology and microdomain characteristics of the nano-composite conversion coatings were characterized by SEM/eds/EPMA, which indicated that the CNT or Ni-CNTs addition promoting the integrity coverage of coatings in a short time.

Surface morphology of titanium-zirconium (Ti-Zr)/Ni-CNT specimens exhibited smooth, compact and little pores. The nano-composite conversion coatings are mainly composed of Al, O, C and Ti elements and contain a small amount of F and Zr elements, which illuminated that CNT or Ni-CNT addition could co-deposit with aluminum and titanium metal oxides.

The study of corrosion resistance of nano-composite conversion coatings and the micro-zone film-formation characteristics would be provided theoretical support for the development of basic research on surface treatment of aluminum alloys.

This study investigated the moderating role of democracy in the relationship between corruption and foreign direct investment. The purpose of this study is to understand whether corruption has different effects on the location decisions of multinational enterprises (MNEs) depending on the regime type.

This study explored how institutional context influenced the impacts of corruption on the location decisions of MNEs, specifically using a sample of Chinese cross-border mergers and acquisitions between 2000 and 2020.

This study assessed the role of democracy in the relationship between corruption and the location decisions of Chinese MNEs. In general, this study found that Chinese MNEs were hindered by host country corruption, but that these detrimental effects were weaker in the presence of more effective democratic institutions.

This study contributes to the literature on institutional factors in international business through its simultaneous investigation of the effects of both democracy and corruption on the location decisions of MNEs. Moreover, there is a prevailing view that Chinese MNEs are willing to enter countries with high corruption, but the results of this study indicate that they are risk-averse in ways similar to their Western counterparts.

This study aims to apply legitimacy theory and self-identity theory to the online food delivery (OFD) app service and then to investigate the impact of green brand legitimacy and biospheric value orientation perceived by customers on eco-friendly behavior.

This study focuses on the mediating role of trust in green brands and its perceived benefits (including psychological and environmental benefits). This study involved an online survey of 445 customers who had experienced using OFD services in the past six months.

The platform's green brand legitimacy and consumer perceived biospheric value orientation positively impact trust in green brands. Trust in green products and services significantly affects customers' perceived benefits and has a positive impact on eco-friendly service using behavior. Mediating effect analysis indicated that brand legitimacy and biospheric value have a positive indirect influence on the psychological benefits of supporting green activities and utilitarian environmental benefits.

The convenience sampling method is used, and its purely quantitative nature may limit the generalization of the research results.

The OFD platform should encourage online catering retailers to use more eco-friendly packages for packaging food and minimize the provision of disposable tableware. The platform manager can provide consumers with knowledge and information on lowering related environmental pollution sources when ordering food.

This study innovatively introduces brand legitimacy into the green consumption literature. This is an essential expansion of the content of brand legitimacy and a supplement for the research field of eco-friendly behavior.

Narrative-driven, choice-based games, games that allow gamers to make decisions regarding the game characters and storylines, can bring forth emotional changes in their players and offer empathy during scenarios that a player may not experience in real-world situations. Therefore, they can be used as tools to help with gender nonconforming (GNC) individuals’ resilience regarding their gender identities. This study explores GNC peoples’ game-playing experiences with choice-based games, especially how such experiences help them gain resilience and shape their gender identities.

This study follows the classic phenomenological approach to understanding the experience of GNC gamers’ resilience experience from their own perspectives. In-depth interviews were conducted with 12 GNC participants, aged between 18 and 34. Each interview lasted 45–90 minutes. Interviews were transcribed and coded using NVivo R1. The essence of meanings was identified using themes and interpreted through qualitative analysis.

This paper identified six gender- and resilience-related common themes within GNC people’s gaming experiences, including 1) character creation: exploring gender identity through an avatar; 2) self-exploration and experimentation in games; 3) resonating experiences; 4) positive inclusive features in games; 5) storytelling and involving the player and 6) your actions have meaning.

The themes, patterns and game features identified in this study may provide insight into potential resilience-building activities for GNC people. They may inform digital mental health interventions, information services and game design practices.

Equity, inclusion and social justice have become a significant theme in today’s society. This study focuses on a marginalized community, GNC people and their mental health and resilience building. Results of the study will contribute to the understanding of this community and may inspire more intervention methods to help them cope with stress and difficult situations.

Research on gaming’s health benefits for the general population has been abundant, but studies about using games to help the LGBTQ+ community have been largely overlooked until recent years. Research on casual games’ mental benefits for LGBTQ+ people is particularly lacking. This research is one of the first in-depth, comprehensive investigations of GNC individuals’ resilience experiences with a particular type of casual video games, choice-based games. The phenomenological study offers rich description of gaming and gender identity exploration from gamers’ viewpoints.

The study uses machine learning techniques to cluster regional retail egg prices after 2000 in China. Furthermore, it combines machine learning results with econometric models to study determinants of cluster affiliation. Eggs are an inexpensiv, nutritious and sustainable animal food. Contextually, China is the largest country in the world in terms of both egg production and consumption. Regional clustering can help governments to imporve the precision of price policies and help producers make better investment decisions. The results are purely driven by data.

The study introduces dynamic time warping (DTW) algorithm which takes into account time series properties to analyze provincial egg prices in China. The results are compared with several other algorithms, such as TADPole. DTW is superior, though it is computationally expensive. After the clustering, a multinomial logit model is run to study the determinants of cluster affiliation.

The study identified three clusters. The first cluster including 12 provinces and the second cluster including 2 provinces are the main egg production provinces and their neighboring provinces in China. The third cluster is mainly egg importing regions. Clusters 1 and 2 have higher price volatility. The authors confirm that due to transaction costs, the importing areas may have less price volatility.

The machine learning techniques could help governments make more precise policies and help producers make better investment decisions.

This is the first paper to use machine learning techniques to cluster food prices. It also combines machine learning and econometric models to better study price dynamics.

The purpose of this paper is to investigate Chinese farmers’ adaptation behavior in the context of the rising cost of labor in agriculture. As the cost of labor increases, farmers will either reallocate their budget to different inputs or change the structure of agricultural production to maximize profit.

The Rural Fixed Point Observation data set between 2004 and 2010 is employed in the empirical analysis of this study. Both the compensated and uncompensated demand elasticities with respect to wages are estimated by adopting the translog cost function and the profit function.

The results show that labor input will drop down significantly as a response to rising wages. Land, fertilizer and intermediate inputs are net complements of labor, whereas machinery appears to be net substitute for labor. In addition, the authors also separate the expansion effect from the substitution effect and find that farmers will shift to grain production with intensive use of fertilizer and from wheat and corn to rice as a response to the rising cost of labor.

This study adopts the classical household model to incorporate various adaptation behaviors of farmers into one framework and decomposes the total effect of the rising cost of labor on input demand into an expansion effect and a substitution effect, which provides a better understanding of farmers’ adaptation behavior.

Public–private partnership (PPP), a project financing arrangement between private investors and the public sector, has revolutionized the approach to the funding and development of public infrastructure worldwide. However, the increasing cases of financial risks and poor financial risk management related to the model threaten the sustainability and financial success of PPP projects leading to huge financial investment losses. This study aims to review existing literature to establish the key measures to control the financial risks of sustainable PPP projects.

A PRISMA-compliant systematic literature review method was used in this study. Data were sourced from academic databases consisting of 56 impactful peer-reviewed journal articles.

The review outcomes demonstrate 41 critical factors (measures) in mitigating the financial risks of sustainable PPP projects. They include minimum revenue guarantee, strategic alliance with private investors, financial transparency and accountability and sound macroeconomic policies. The principal results of the study were categorized and conceptualized into a financial risk management maturity model for sustainable PPP projects. Lastly, the study reveals that further studies and project policies must focus more on addressing financial challenges relating to climate risks, and health and safety concerns such as COVID-19 outbreak that have negative impacts on PPP projects.

The results provide essential research gaps and directions for future studies on measures to mitigate the financial risks of sustainable PPP projects. However, this study used small but significant existing publications.

A checklist and a conceptual maturity model are provided in this study to help practitioners to learn and improve upon their practices to mitigate the financial risks of sustainable PPP projects.

This study contributes to managerial measures to reduce huge losses in financial investments of PPP projects and the attainment of sustainability in public infrastructure projects with a financial risk maturity model.

In the context of China, the purpose of this paper is to empirically answer three related questions: Could circular agriculture (CA) attain economic, ecological and social benefits simultaneously? What is key to a successful CA business in emerging economies? And who plays the vital role in building and sustaining a circular business?

The paper is based on a field study and looks at a farm in China. It uses a triangulation methodology to collect information. Besides longitudinal filed work at the farm, the researchers have also interviewed multiple stakeholders and conducted field research at the local markets.

With concrete performance data, the study proves that a circular approach can help achieve ecological, economic and social goals together. It shows that economic viability is essential to succeeding in circular operation, sufficient production pathways are required to make such operation sustainable, and entrepreneurship is key to build and grow a circular business.

The findings point to the crucial role of entrepreneurship in promoting the circular model in emerging economies. These findings, however, may not be readily generalizable, given the limitations of the case study approach.

The study highlights a few areas in which government assistance can make a difference, including financial incentives, information provision, technical support and most importantly the creation of a positive environment for entrepreneurial development.

While prior research emphasizes the role of government in promoting circular economy in developing and emerging markets, the study proves that entrepreneurship is key to turning government initiatives into economically viable and sustainable circular operation.