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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.

In this paper, the effectiveness of a number of active devices for the control of shock waves on transonic aerofoils is investigated using numerical solutions of the Reynolds‐averaged Navier‐Stokes equations. A brief description of the flow model and the numerical method is presented including, in particular, the boundary condition modelling and the numerical treatment for surface mass transfer. Comparisons with experimental data have been made where possible to validate the numerical study before some systematic numerical simulations for a parametric study. The effects of surface suction, blowing, and local modification of the surface contour (bump) on aerofoil aerodynamic performance have been studied extensively regarding the control location, the mass flow strength and the bump height. The numerical simulations highlight the benefits and drawbacks of the various control devices for transonic aerodynamic performance and identify the key design parameters for optimisation.

The purpose of our study is to explore the “idiosyncratic volatility puzzle” in Chinese stock market from the perspective of investors' heterogeneous beliefs. To delve into the relationship between idiosyncratic volatility and investors' heterogeneous beliefs, and uncover the ability of heterogeneous beliefs, as well as to explain the “idiosyncratic volatility puzzle”, we construct our study as follows.

Our study adopts the unexpected trading volume as proxies of heterogeneity, the residual of Fama–French three-factor model as proxies of idiosyncratic volatility. Portfolio strategies and Fama–MacBeth regression are used to investigate the relationship between the two proxies and stock returns in Chinese A-share market.

Investors' heterogeneous beliefs, as an intermediary variable, are positively correlated with idiosyncratic volatility. Meanwhile, it could better demonstrate the negative correlation between the idiosyncratic volatility and future stock returns. It is one of the economic mechanisms linking idiosyncratic volatility to subsequent stock returns, which can account for 11.28% of the puzzle.

The findings indicate that idiosyncratic volatility is significantly and positively correlated with heterogeneous beliefs and that heterogeneous beliefs are effective intervening variables to explain the “idiosyncratic volatility puzzle”.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

Enterprises build online product community to expect users to contribute: opinion sharing (content contribution) and product consumption (product contribution). Previous literature rarely focused on both. The purpose of this paper is to explain user contribution mechanism by identifying content contribution and product contribution.

This research chose Xiaomi-hosted online product community (bbs.xiaomi.cn) and Huawei-hosted online product community (club.huawei.com) where users can freely share ideas and buy products at the same time. Data were crawled from 109,665 community users to construct dependent variable measurement, and 611 questionnaires were used to verify research hypotheses.

The results indicate that both cognitive needs and personal integration needs have a significant positive impact on browse behavior; social integration needs and hedonic needs have a significant positive impact on content contribution behavior. Browse behavior not only directly affects but also indirectly influences product contribution through content contribution behavior.

Findings of this research provide community managers with useful insights into the relationship between content contribution and product contribution.

This study explains the formation mechanism of user product contribution and reveals the relationship between user content contribution and product contribution in online product community. This paper provides a different way of theorizing user contributions by incorporating uses and gratifications theory into the “Motivation-Behavior-Result” framework.

The OpenURL standard provides a mechanism to transport metadata or identifiers of a digital item from one resource to another as well as a way to construct links in a dynamic linking environment. The OpenURL standard provides a means of integrating electronic resources. This article first describes some integration issues for electronic resources in the library and continues to discuss types of URL before giving an overview of the OpenURL standard and the OpenURL linking system, the link resolver. The major OpenURL linking products and host solution options are described, and the impact of the OpenURL standard and OpenURL linking system on library users and library services are discussed. New developments in the OpenURL standard and OpenURL linking systems are provided in the concluding section.

This study aims to evaluate the thermal performance of sodium alginate (SA) aerogel attached to nano SiO₂ and its radiative cooling effect on firefighting clothing without environmental pollution.

SA/SiO₂ aerogel with refractory heat insulation and enhanced radiative cooling performance was fabricated by freeze-drying method, which can be used in firefighting clothing. The microstructure, chemical composition, thermal stability, and thermal emissivity were analyzed using Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analyzer and infrared emissivity measurement instrument. The radiative cooling effect of aerogel was studied using thermal infrared imager and thermocouple.

When the addition of SiO₂ is 25% of SA, the prepared aerogel has excellent heat insulation and a high radiative cooling effect. Under a clear sky, the temperature of SA/SiO₂ aerogel is 9.4°C lower than that of pure SA aerogel and 22.1°C lower than that of the simulated environment. In addition, aerogel has more exceptional heat insulation effect than other common fabrics in the heat insulation performance test.

SA/SiO₂ aerogel has passive radiative cooling function, which can efficaciously economize global energy, and it is paramount to environment-friendly cooling.

This method could pave the way for high-performance cooling materials designed for firefighting clothing to keep maintain the wearing comfort of firefighters.

SA/SiO₂ aerogel used in firefighting clothing can release heat to the low-temperature outer space in the form of thermal radiation to achieve its own cooling purpose, without additional energy supply.

Systemic risk is of concern for economic welfare as it can lower the credit supply to all the sectors within an economy. This study examines for the first time the complete hierarchy of variables that drive systemic risk during normal and crisis periods in Pakistan, a developing economy.

Secondary data of the bank, sector and country variables are used for the purpose of the analysis spanning from 2000 to 2020. Systemic risk is computed using marginal expected shortfall (MES). One-step and two-step system GMM is performed to estimate the impact of firm, sector and country-level variables on systemic risk.

The findings of the study highlight that sector-level variables are also highly significant in explaining the systemic risk dynamics along with bank and country-level variables. In addition, economic sensitivity influences the significance level of variables across crisis and post-crisis periods and modifies the direction of relationships in some instances.

The study examines the systemic risk of a developing economy, and findings may not be generalizable to developed economies.

The outcome of the study provides a comprehensive framework for the central bank and other regulatory authorities that can be translated into timely policies to avoid systemic financial crisis.

The negative externalities generated by systemic risk also affect the general public. The study results can be used to avoid the systemic financial crisis and resultantly save the loss of the general public's hard-earned holdings.

The firm, sector and country-level variables are modeled for the first time to estimate systemic risk across different economic conditions in a developing economy, Pakistan. The study can also act as a reference for researchers in developed economies as well regarding the role of sector-level variables in explaining systemic risk.

This study aims to fabricate a novel electromagnetic interference (EMI) shielding composite aerogel with both thermal insulation and high temperature warning functions.

An emerging bio-based polypyrrole (PPy) gel/Fe₃O₄/calcium alginate (PFC) EMI shielding composite aerogel was prepared by freeze-drying and in situ polymerization method. First, Fe₃O₄/calcium alginate (CA) aerogel was obtained by freeze-drying the Fe₃O₄/CA mixture. Then, PPy/Fe₃O₄/CA was obtained by synthesizing PPy on the surface of CA/Fe₃O₄ aerogel through in situ polymerization. Finally, PPy/Fe₃O₄/CA was immersed in porphyrin solution (cross-linking agent) to get the final PFC EMI shielding composite aerogel.

Due to the matched impedance between Fe₃O₄ and PPy, the EMI shielding performance of PFC composite aerogel can reach up to −8 dB. In addition, the PFC EMI shielding composite aerogel also shows excellent self-extinguishing and thermal insulation properties. After leaving the flame, the burning PFC aerogel is quickly extinguished. When the PFC aerogel is placed on the heating plate at 230 °C, the temperature on the side of the aerogel away from the heating plate is only 90.3 °C after 5 min of heating. The electrical resistance of the PFC composite aerogel can be reduced from 3.62 × 104 O to 5 × 102 O to trigger the warning light after 3 s of exposure to the alcohol lamp flame. This reversible thermal resistance response characteristic can be used to give an early warning signal when the PFC encounters high temperature or flame.

This work provides a novel strategy for designing a multifunctional EMI shielding composite aerogel with repeatable high temperature warning performance. This PFC composite aerogel shows potential applications in the prevention of material combustion in high temperature electromagnetic environments.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.