Guangdong University Of Finances and Economics

Abstract In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of deep convolutional neural network (CNN). The CNN has superior features for autonomous learning and expression, and feature extraction from original input data can be realized by means of training CNN models that match practical applications. Due to the rapid progress in deep learning technology, the structure of CNN is becoming more and more complex and diverse. Consequently, it gradually replaces the traditional machine learning methods. This paper presents an elementary understanding of CNN components and their functions, including input layers, convolution layers, pooling layers, activation functions, batch normalization, dropout, fully connected layers, and output layers. On this basis, this paper gives a comprehensive overview of the past and current research status of the applications of CNN models in computer vision fields, e.g., image classification, object detection, and video prediction. In addition, we summarize the challenges and solutions of the deep CNN, and future research directions are also discussed.

ABSTRACT This paper studies the asset pricing implications of industrial pollution. A long‐short portfolio constructed from firms with high versus low toxic emission intensity within an industry generates an average annual return of 4.42%, which remains significant after controlling for risk factors. This pollution premium cannot be explained by existing systematic risks, investor preferences, market sentiment, political connections, or corporate governance. We propose and model a new systematic risk related to environmental policy uncertainty. We use the growth in environmental litigation penalties to measure regime change risk and find that it helps price the cross section of emission portfolios' returns.

Abstract Green innovation has become the main path toward achieving corporate sustainable development. Despite the centrality of organizational learning in firms pursuing green activities, little research has considered its role in the relationship between stakeholder pressures and green innovation. By combining stakeholder theory and organizational learning theory, this study explores whether environmental pressures from different stakeholders influence green innovation differently and how this is further mediated by organizational learning. From a sample of 259 Chinese manufacturing firms, we find that consumer pressure has a greater positive effect on green product innovation than regulation pressure, whereas regulation pressure is more positively related to green process innovation than consumer pressure. Moreover, these two relationships are partially mediated by exploration learning and exploitation learning, respectively. These findings advance the existing research on the stakeholder pressures–green innovation linkage by revealing that consumer and regulation pressures influence green product innovation and green process innovation differently and through different organizational learning approaches.

This study aims to explore the impact of the Covid-19 pandemic on tourists' travel risk and management perceptions. Driven on the effect of the pandemic, we investigate tourists' travel risk and management perceptions and its effect on society using a sample of 716 respondents. The data was collected through social media platforms using a representative sampling method and analyzed applying the PLS-SEM tool. The findings reveal that Covid-19 pandemic has greatly affected travel risk and management perceptions. Travel risk and management perception had a significant association with risk management, service delivery, transportation patterns, distribution channels, avoidance of overpopulated destinations, and hygiene and safety. The results also identified the mediating effect of travel risk and management perceptions. The finding of this study contributes to tourism crises and provides future research insights in the travel and tourism sector and response to change tourists' travel risk and management perceptions in the post-covid recovery period.

Purpose This article aims to study the role of personal values as moderators of the antecedents of consumers' “green” buying intentions in the context of Chinese consumers' inclination to buy organic food. Design/methodology/approach Ordinary Chinese consumers ( n =479) were intercepted and filled out a questionnaire outside upscale supermarkets in Guangzhou. Multigroup structural equation modeling was used to test hypotheses about personal values' moderating effect in the theory of planned behavior. Findings Self‐transcendence values moderate the relationship between two antecedents and behavioral intentions: the attitude towards buying organic food and perceived behavioral control. Both of these antecedents have a stronger impact on intentions among consumers with strong self‐transcendence values than among consumers with weak ones. Research limitations/implications The study is based on a single consumer survey collected from a convenience sample of consumers from one Chinese city. Hence, care needs to be exercised when making inferences about causality and representativeness. Practical implications Study results have direct implications for the marketing of organic food. As the food safety problem in China is getting more severe and environmental issues are increasing on the political and public agendas, the consumption of organic food is being increasingly advocated by both the government and food producers, as a healthy and environment‐friendly alternative, which also may contribute positively to the development of the economy. Originality/value This article extends the rare literature analyzing Chinese consumers' inclination to buy organic food. It also extends the understanding of the role of personal values as moderators of antecedents of consumers' buying intentions for “green” products.

An efficient use of energy is the pre-condition for economic development. But excessive use of fossil fuel harms the environment. As renewable energy emits no or low greenhouse gases, more countries are trying to increase the use of energies from renewable sources. At the same time, no matter developed or developing, nations have to maintain economic growth. By collecting SCI/SSCI indexed peer-reviewed journal articles, this article systematically reviews the consumption nexus of renewable energy and economic growth. A total of 46 articles have been reviewed following the PRISMA guidelines from 2010 to 2021. Our review research shows that renewable energy does not hinder economic growth for both developing and developed countries, whereas, there is little significance of consuming renewable energy (threshold level) on economic growth for developed countries.

In today's increasingly interconnected world, co‐opetition has emerged as a new business practice among many high‐tech firms. The boundaries between cooperation and competition becomes vague, and rivals engage in collaborative activities. This study develops an analytical model to investigate the dual sourcing decision of the original equipment manufacturer (OEM) in the presence of a competitive supplier (i.e., frenemy) as well as a non‐competitive supplier who nevertheless suffers from unreliable production yield. We study the competitive supplier's dual channel decision if it prefers operating both component‐selling business and self‐branded business, and find that the OEM always prefers supplier diversification even though the additional non‐competitive supplier is unreliable. Interestingly, our results reveal that the non‐competitive supplier's expected profit is unimodal in its production technology level, which suggests the non‐competitive supplier may not have incentive to improve its production technology once it reaches a threshold. Furthermore, we analyze the credibility of the competitive supplier's threat to terminate the supply of the components to OEM as a response of OEM's engagement of a new supplier. We show that this termination of component‐selling business by competitive supplier is a non‐credible threat to prevent OEM from seeking the alternative supplier.

Research on learning engagement and cognitive load theory have proceeded in parallel with little cross-over of ideas. The aim of this research was to test an integrative model that examines how prior knowledge influences learning engagement via cognitive load and help-seeking strategies. A sample of 356 students from two middle schools in the north of China participated in the study. Analyses using structural equation modeling revealed that prior knowledge was positively associated with learning engagement, and that this relationship was mediated by cognitive load and instrumental help-seeking. Cognitive load also mediated the impact of prior knowledge on instrumental help-seeking, executive help-seeking and avoidance of help-seeking. The study shows that students with more prior knowledge and lower cognitive load are able to exercise higher levels of instrumental help-seeking, leading to good quality learning engagement. On the other hand, students with less prior knowledge and higher cognitive load are less able to engage in instrumental help-seeking, leading to lower engagement. Based on the research findings, recommendations for how teachers can improve learning engagement through decreasing cognitive load are discussed.

In this paper, we propose a blind image watermarking algorithm based on the multiband wavelet transformation and the empirical mode decomposition. Unlike the watermark algorithms based on the traditional two-band wavelet transform, where the watermark bits are embedded directly on the wavelet coefficients, in the proposed scheme, we embed the watermark bits in the mean trend of some middle-frequency subimages in the wavelet domain. We further select appropriate dilation factor and filters in the multiband wavelet transform to achieve better performance in terms of perceptually invisibility and the robustness of the watermark. The experimental results show that the proposed blind watermarking scheme is robust against JPEG compression, Gaussian noise, salt and pepper noise, median filtering, and ConvFilter attacks. The comparison analysis demonstrate that our scheme has better performance than the watermarking schemes reported recently.

This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric output constraint, and system parameter uncertainties. Using the backstepping technique, a boundary control scheme is designed to suppress the vibration and regulate the angle of the spacecraft. A modified asymmetric barrier Lyapunov function is utilized to ensure that the output constraint is never transgressed. Considering the system robustness, neural networks are used to handle the system parameter uncertainties and compensate for the effect of input nonlinearity. With the proposed adaptive neural network control law, the stability of the closed-loop system is proved based on the Lyapunov analysis, and numerical simulations are carried out to show the validity of the developed control scheme.

Abstract When addressing corporate sustainability challenges, artificial intelligence (AI) is a double-edged sword. AI can make significant progress on the most complicated environmental and social problems faced by humans. On the other hand, the efficiencies and innovations generated by AI may also bring new risks, such as automated bias and conflicts with human ethics. We argue that companies and governments should make collective efforts to address sustainability challenges and risks brought by AI. Accountable and sustainable AI can be achieved through a proactive regulatory framework supported by rigorous corporate policies and reports. Given the rapidly evolving nature of this technology, we propose a harmonised and risk-based regulatory approach that accommodates diverse AI solutions to achieve the common good. Ensuring an adequate level of technological neutrality and proportionality of the regulation is the key to mitigating the wide range of potential risks inherent to the use of AI. Instead of promoting sustainability, unregulated AI would be a threat since it would not be possible to effectively monitor its effects on the economy, society and environment. Such a suitable regulatory framework would not only create a consensus concerning the risks to avoid and how to do so but also include enforcement mechanisms to ensure a trustworthy and ethical use of AI in the boardroom. Once this objective is achieved, it will be possible to refer to this technological development as a common good in itself that constitutes an essential asset to human development.

It is generally accepted that the essential goal of many-objective optimization is the balance between convergence and diversity. For constrained many-objective optimization problems (CMaOPs), the feasibility of solutions should be considered as well. Then the real challenge of constrained many-objective optimization can be generalized to the balance among convergence, diversity, and feasibility. In this paper, a tri-goal evolution framework is proposed for CMaOPs. The proposed framework carefully designs two indicators for convergence and diversity, respectively, and converts the constraints into the third indicator for feasibility. Since the essential goal of constrained many-objective optimization is to balance convergence, diversity, and feasibility, the philosophy of the proposed framework matches the essential goal of constrained many-objective optimization well. Thus, it is natural to use the proposed framework to deal with CMaOPs. Further, the proposed framework is conceptually simple and easy to instantiate for constrained many-objective optimization. A variety of balance schemes and ranking methods can be used to achieve the balance among convergence, diversity and feasibility. Three typical instantiations of the proposed framework are then designed. Experimental results on a constrained many-objective optimization test suite show that the proposed framework is highly competitive with existing state-of-the-art constrained many-objective evolutionary algorithms for CMaOPs.

Bioenergy is a renewable energy source that saves from fossil fuel dependence. Therefore, it is important to increase the efficiency of bioenergy investments to create an environmentally sustainable energy supply. This paper aims to identify economic indicators significant in forecasting the supply of bioenergy. Considering this goal, an integrated evaluation has been performed for 17 developed economies using the Random Forest method and the Fuzzy Decision-Making Trial and Evaluation Laboratory (QROF-DEMATEL) method. The main contribution of this study is conducting analysis by using both quantitative and qualitative data. Additionally, the coherence of the results made with the QROF-DEMATEL method is also verified by implementing a sensitivity analysis. The results of both approaches are quite similar and provide information about the reliability of the findings. This situation demonstrates that for the development of bioenergy investments, firstly, countries’ macroeconomic conditions should be improved. Consequently, economic growth and unemployment (weighting results - 0.159 and 0.155) should be primarily considered for the bioenergy supply forecast.

The logistics industry around the world has proliferated over recent years as a large number of business organizations have come to recognize the importance of logistics. Cost control used to be emphasized to remain competitive, but recently green logistics has gained attention with the awareness of the integration of economy and society as a whole. Nowadays, green logistics is a useful concept to improve the sustainability of logistics operations, and its related policies and theoretical research have been investigated and explored. However, the practical applications of green logistics are impeded by real-time data sharing, which is common in the logistics industry. Blockchain technology is adopted to address this challenge and enable data sharing among related stakeholders. This paper presents a reference framework for green logistics based on blockchain to reach the sustainable operations of logistics, with the integration of the Internet of Things and big data. Finally, potential benefits and limitations are analyzed when implementing this framework.

The Internet of Things (IoT) changed our lives with huge amounts of data production. Due to source-limited IoT devices, one of the best ways to process the data is cloud storage. However, a series of security and privacy issues arise, such as illegal data access, data tampering, and privacy leak. Though symmetric encryption can guarantee data confidentiality, it cannot realize fine-grained data sharing and searching. The keyword-based searchable attribute-based encryption (KSABE) can achieve data confidentiality and fine-grained access control. More importantly, it realizes a keyword-based search for data users. However, the heavy decryption computation burden and the management of massive user keys appear when implementing attribute-based encryption schemes to IoT. Therefore, this article proposes a blockchain-aided searchable attribute-based encryption (BC-SABE) with efficient revocation and decryption, where the traditional centralized server is replaced with a decentralized blockchain system being in charge of the threshold parameter generation, key management, and user revocation. All revocation tasks are done by the blockchain and it is on longer necessary for ciphertext reencryption and key update. Moreover, users utilize the coalition blockchain to generate partial tokens. Besides, the cloud server contained in our scheme not only stores the massive encrypted data but also performs search and predecryption for users who only require one exponentiation in the group G to decrypt fully. Security analyses prove that our scheme realizes the security under the chosen plaintext attack and the chosen keyword attack. Simulations show that the decryption and token generation cost of our scheme are preferable.

Abstract Metallic zinc (Zn) for next‐generation aqueous batteries often suffers from severe dendrite growth, unfavorable hydrogen evolution, and self‐corrosion, especially in alkaline electrolyte. Herein, the authors demonstrate a facile and efficient strategy to tackle above issues by electrochemically depositing Zn onto the Cu–Zn alloy surface (CZ‐Zn). The zincophilic Cu sites throughout the Cu–Zn alloy can remarkably enhance the Zn 2+ adsorption and promote homogeneous Zn nucleation on its surface, endowing it with highly reversible Zn plating/stripping chemistry. Furthermore, the intrinsically inert nature of Cu toward hydrogen evolution reaction (HER) and high dezincification potential of the Cu‐Zn alloy can effectively alleviate the hydrogen evolution and Zn corrosion in aqueous electrolyte. Consequently, the symmetric cells with the CZ‐Zn electrodes exhibit outstanding cycling life in both alkaline and neutral electrolytes, which can operate steadily over 800 h and 1600 h at 2.5 mAh cm –2 , respectively, far surpassing the pristine Zn electrodes. In addition, a high‐performance alkaline full battery with ultra‐long cyclic stability (no capacity degradation after 5000 cycles) and excellent Coulombic efficiency (CE) (100%) is achieved by pairing this CZ‐Zn anode with a Ni 3 S 2 @polyaniline cathode. This study sheds light on the design of robust and ultra‐stable Zn anodes for the state‐of‐art aqueous energy storage devices.

A luminescent lanthanide MOF-based thin film was fabricated by electrodeposition in an anhydride system and this film can be used as a highly selective sensor for CO 3 2− in aqueous solution.

This stimulating new book, as the premier work introducing bilingual lexicography from a communicative perspective, is launched to represent original thinking and innovative theorization in the field of bilingual lexicography. It treats the bilingual dictionary as a system of intercultural communication and bilingual dictionary making as a dynamic process realized by sets of choices, characterizing the overall nature of the dictionary. It examines the dictionary and dictionary making by using a model of lexicography which stresses the three-way relationship of compiler, dictionary context and user and incorporates them into a unified coherent framework. Throughout the study, special focus is on English and Chinese bilingual lexicography. It will serve not only as a valuable guide to those interested in dictionary compilation and theoretical inquiries but also as a textbook for undergraduate and postgraduate courses in bilingual lexicography.

Both carbon tax and cap-and-trade systems are widely applied to reduce emission. This article compares the clean innovation effects of carbon tax with cap-and-trade systems by a static optimal model. Firstly, both cap-and-trade system and carbon tax stimulates clean innovation and reduce emission. Secondly, cap-and-trade system is more efficient to reduce emission and to promote clean innovation than carbon tax. Finally, firms undertake a loss under carbon tax, while the effects of cap-and-trade system on firms' profits are uncertain, which depends on the carbon cap. In summary, this article supports cap-and-trade system to cope with global climate change, but the regulator should choose the suitable emission cap and carbon trading price to guarantee the efficiency of cap-and-trade system. So, different purposes match with different carbon emission tax policies.

Herein, we report an effective and simple strategy to greatly improve the photoactivity of CdO nanorods (NRs) by decorating them with CdS. The CdO–CdS heterostructured NRs grown on a FTO substrate exhibited substantially higher visible-light-driven photoactivity for a PEC cell and the degradation of methylene blue (MB) solution compared to CdO NRs.