Jiangxi University of Finance and Economics

As a new enterprise development model, digitization is of great significance to the development of economy and society. Using the data of relevant listed companies from 2012 to 2020, the panel measurement model is used to investigate the relationship between digital transformation and enterprise performance to further reveal the internal law of whether digital transformation helps to stimulate innovation momentum. The results show that digital transformation has greatly improved the performance of enterprises, and it can stimulate the momentum of enterprise innovation. Reducing costs, increasing revenue, improving efficiency, and encouraging innovation are the main paths for digital transformation to enable the development of enterprises, among which the policy effect of enterprise innovation is the most significant. This research is of great significance to improve the user demand orientation of enterprise innovation and research and development, as well as to realize the high-quality innovation and development of enterprises.

Under-exposure introduces a series of visual degradation, i.e. decreased visibility, intensive noise, and biased color, etc. To address these problems, we propose a novel semi-supervised learning approach for low-light image enhancement. A deep recursive band network (DRBN) is proposed to recover a linear band representation of an enhanced normal-light image with paired low/normal-light images, and then obtain an improved one by recomposing the given bands via another learnable linear transformation based on a perceptual quality-driven adversarial learning with unpaired data. The architecture is powerful and flexible to have the merit of training with both paired and unpaired data. On one hand, the proposed network is well designed to extract a series of coarse-to-fine band representations, whose estimations are mutually beneficial in a recursive process. On the other hand, the extracted band representation of the enhanced image in the first stage of DRBN (recursive band learning) bridges the gap between the restoration knowledge of paired data and the perceptual quality preference to real high-quality images. Its second stage (band recomposition) learns to recompose the band representation towards fitting perceptual properties of high-quality images via adversarial learning. With the help of this two-stage design, our approach generates enhanced results with well-reconstructed details and visually promising contrast and color distributions. Qualitative and quantitative evaluations demonstrate the superiority of our DRBN.

We show, based on ratings by finance journal reviewers of generated output, that the recently released AI chatbot ChatGPT can significantly assist with finance research. In principle, these results should be generalisable across research domains. There are clear advantages for idea generation and data identification. The technology, however, is weaker on literature synthesis and developing appropriate testing frameworks. Importantly, we further demonstrate that the extent of private data and researcher domain expertise input, are key factors in determining the quality of output. We conclude by considering the implications, particularly the ethical implications, which arise from this new technology.

This study empirically examines the dynamic relationships among tourism, economic growth, and CO 2 emissions and compares the effects of tourism on economic growth and CO 2 emissions between developed and developing economies. By employing robust panel econometric techniques, the results show that tourism has significant positive impacts on economic growth for both developed and developing economies, supporting the prevailing hypothesis of tourism-led economic growth. The results also reveal that the impact of tourism on CO 2 emissions is reducing much faster in developed economies than in developing economies, providing evidence of the environmental Kuznets curve (EKC) hypothesis on the link between tourism growth and CO 2 emissions. Our findings demonstrate the importance of the classification of countries by economic development level to obtain a deeper understanding of relationships among tourism, economic growth, and CO 2 emissions. Policy implications are provided and discussed.

Although nearly two decades of research have provided support for the social identity approach to leadership, most previous work has focused on leaders' identity prototypicality while neglecting the assessment of other equally important dimensions of social identity management. However, recent theoretical developments have argued that in order to mobilize and direct followers' energies, leaders need not only to ‘be one of us’ (identity prototypicality), but also to ‘do it for us’ (identity advancement), to ‘craft a sense of us’ (identity entrepreneurship), and to ‘embed a sense of us’ (identity impresarioship). In the present research we develop and validate an Identity Leadership Inventory (ILI) that assesses these dimensions in different contexts and with diverse samples from the US, China, and Belgium. Study 1 demonstrates that the scale has content validity such that the items meaningfully differentiate between the four dimensions. Studies 2, 3, and 4 provide evidence for the scale's construct validity (distinguishing between dimensions), discriminant validity (distinguishing identity leadership from authentic leadership, leaders' charisma, and perceived leader quality), and criterion validity (relating the ILI to key leadership outcomes). We conclude that by assessing multiple facets of leaders' social identity management the ILI has significant utility for both theory and practice.

Economic transitions in countries that move from state planning and redistribution to market exchange create business opportunities but also uncertainty, because many interdependent factors—modes of exchange, types of products, and forms of organizations—are in flux. Uncertainty is even greater when the country’s political institutions remain authoritarian because the rule of law is weak and state bureaucrats retain power over the economy. This study of listed firms in China, which has recently seen economic transition but persistent authoritarianism, shows that in such contexts, firms can reduce uncertainty by developing relationships with state bureaucrats, which help firms learn how state bureaucracies operate and engender trust between firms and bureaucrats. Together, knowledge and trust stabilize operations and help persuade bureaucrats to lighten regulatory burdens, grant firms access to state-controlled resources, and improve government oversight. Our results show that as economic transitions proceed and uncertainty increases, business–state ties increasingly improve firm performance. We also investigate two likely contingencies, industry and firm size, and two important causal mechanisms, access to bank loans and protection from related-party loans, and show that the value of business–state relations varies over time, depending on the trajectory of both economic and political institutions.

Land degradation is a global issue receiving much attention currently. In order to objectively reveal the research situation of land degradation, bibliometrix and biblioshiny software packages have been used to conduct data mining and quantitative analysis on research papers in the fields of land degradation during 1990–2019 (data update time was 8 April 2019) in the Web of Science core collection database. The results show that: (1) during the past 20 years, the number of papers on land degradation has increased. According to the number of articles, it is divided into four stages: a low-production exploration period, a developmental sprout period, expansion of the promotion period, and a high-yield active period. (2) Land-degradation research covers 93 countries or regions. The top five countries in terms of research volume are China, the United States, the United Kingdom, Germany, and Australia. China, the United States, and the United Kingdom are the most important countries for international cooperation in the field of land degradation. However, cooperation between countries is not very close overall. (3) Land degradation, degradation, desertification, remote sensing, soil erosion, and soil degradation are high-frequency keywords in the field of land degradation in recent years. (4) The research hotspots in the field of land degradation mainly focus on research directions such as restoration and reconstruction of land degradation, and sustainable management of land resources. (5) The themes of various periods in the field of land degradation are diversified, and the evolutionary relationship is complex. There are 15 evolutionary paths with regard to dynamic monitoring of land degradation, environmental governance of land degradation, and responses of land degradation to land-use change. Finally, the paper concludes that the research directions on land degradation in future include the process, mechanism, and effect of land degradation, the application of new technologies, new monitoring methods for land degradation, theory enhancement, methods and models of ecological restoration, reconstruction of degraded land, multidisciplinary integrated system research, constructing a policy guarantee system for the reconstruction of degraded land, and strengthening research on land resource engineering.

Public–private partnerships (PPPs) have become popular tools to deliver infrastructure and public services around the world. As an innovative public procurement approach, PPPs have drawn considerable attention from academic circles. In order to enhance our knowledge of PPPs, the authors conducted a systematic literature review of articles published in international journals of the Public Administration (PA) discipline. Four main topics in this discipline are identified by means of social network analysis, including PPP concept, risk sharing amongst PPP participants, the drivers of PPP adoption, and PPP performance. Seven propositions about the four topics are summarized. Directions for future research are also considered.

The Pythagorean fuzzy set, as a new extension of intuitionistic fuzzy set, has recently been developed to manage the complex uncertainty in practical group decision problems. The purpose of this article is to develop a new decision method based on similarity measure to address multiple criteria group decision making problems within Pythagorean fuzzy environment based on Pythagorean fuzzy numbers (PFNs). The contribution of this article is fivefold: (1) An accuracy function of PFNs is defined and a new ranking method for PFNs is proposed; (2) new Pythagorean fuzzy aggregating operators are developed; (3) a novel similarity measure for PFNs is presented, and some desirable properties are discussed; (4) a simple and effective Pythagorean fuzzy group decision method is introduced; and (5) The proposed method is applied to address the selection problem of photovoltaic cells.

As smartphones become people's primary cameras to take photos, the quality of their cameras and the associated computational photography modules has become a de facto standard in evaluating and ranking smartphones in the consumer market. We conduct so far the most comprehensive study of perceptual quality assessment of smartphone photography. We introduce the Smartphone Photography Attribute and Quality (SPAQ) database, consisting of 11,125 pictures taken by 66 smartphones, where each image is attached with so far the richest annotations. Specifically, we collect a series of human opinions for each image, including image quality, image attributes (brightness, colorfulness, contrast, noisiness, and sharpness), and scene category labels (animal, cityscape, human, indoor scene, landscape, night scene, plant, still life, and others) in a well-controlled laboratory environment. The exchangeable image file format (EXIF) data for all images are also recorded to aid deeper analysis. We also make the first attempts using the database to train blind image quality assessment (BIQA) models constructed by baseline and multi-task deep neural networks. The results provide useful insights on how EXIF data, image attributes and high-level semantics interact with image quality, how next-generation BIQA models can be designed, and how better computational photography systems can be optimized on mobile devices. The database along with the proposed BIQA models are available at https://github.com/h4nwei/SPAQ.

Contrast distortion is often a determining factor in human perception of image quality, but little investigation has been dedicated to quality assessment of contrast-distorted images without assuming the availability of a perfect-quality reference image. In this letter, we propose a simple but effective method for no-reference quality assessment of contrast distorted images based on the principle of natural scene statistics (NSS). A large scale image database is employed to build NSS models based on moment and entropy features. The quality of a contrast-distorted image is then evaluated based on its unnaturalness characterized by the degree of deviation from the NSS models. Support vector regression (SVR) is employed to predict human mean opinion score (MOS) from multiple NSS features as the input. Experiments based on three publicly available databases demonstrate the promising performance of the proposed method.

ABSTRACT A variety of mathematical forms have been developed to characterize demand functions which depend on a firm's operational and marketing activities. Such demand functions are being increasingly used by researchers in economics and different functional areas of business. We provide a comprehensive survey of commonly used demand models which depend on (i) price, (ii) rebate, (iii) lead time, (iv) space, (v) quality, and (vi) advertising. Our survey includes single firm–demand models in each category, as well as game theoretic multifirm models involving strategic interaction among the firms. We observe that certain types of functional forms, such as linear, power/iso‐elastic, multinomial logit, and multiplicative competitive interaction, have been widely used to construct various demand models in all six categories, but that a large majority of publications deal with categories (i) and (v) of demand models. For each of the six categories, we survey relevant functional forms in the representative papers, and discuss the main properties, the advantages, the disadvantages, and comment on possible future research directions. We also present discussions of the applications of these analytical demand models in empirical studies. The article ends with a summary of our major findings.

This study presents a summary of green building research through a bibliometric approach. A total of 2980 articles published in 2000–2016 were reviewed and analyzed. The results indicated that green building research had been concentrated on the subject categories of engineering, environmental sciences & ecology, and construction & building technology, and the keywords ‘building envelope’ and ‘living wall’ obtained citation bursts in the recent years. Additionally, based on the cluster analysis and content analysis, the hot research topics were identified: green and cool roof, vertical greening systems, water efficiency, occupants’ comfort and satisfaction, financial benefits of green building, life cycle assessment and rating systems, green retrofit, green building project delivery, and information and communication technologies in green building. Knowledge gaps were detected in the areas of corporate social responsibility, the validation of real performance of green building, the ICT application in green building, as well as the safety and health risks in the construction process of green projects. Future research directions are recommended to fill these gaps and extend the body of green building research.

Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem. In this paper, we propose an end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs. A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships, and is further embedded in a Dense Attention Fluid (DAF) structure that enables shallow attention cues flow into deep layers to guide the generation of high-level feature attention maps. Specifically, the GCA module is composed of two key components, where the global feature aggregation module achieves mutual reinforcement of salient feature embeddings from any two spatial locations, and the cascaded pyramid attention module tackles the scale variation issue by building up a cascaded pyramid framework to progressively refine the attention map in a coarse-to-fine manner. In addition, we construct a new and challenging optical RSI dataset for SOD that contains 2,000 images with pixel-wise saliency annotations, which is currently the largest publicly available benchmark. Extensive experiments demonstrate that our proposed DAFNet significantly outperforms the existing state-of-the-art SOD competitors. https://github.com/rmcong/DAFNet_TIP20.

Is clean energy a safe haven for cryptocurrencies, or vice versa? In this paper, we investigate the hedge and safe haven property of a wide range of clean energy indices against two distinct types of cryptocurrencies based on their energy consumption levels, termed “dirty” and “clean”. Statistical evidence shows that clean energy is not a direct hedge for either of types. However, it serves as at least a weak safe haven for both in extreme bearish markets. Moreover, clean energy is more likely to be a safe haven for dirty cryptocurrencies than clean cryptocurrencies during increased uncertainty. We further study the spillover patterns among clean energy, cryptocurrency, stock, and gold markets. Weak connectedness is found between clean energy and cryptocurrencies which implies the potential use of clean energy as a hedge and diversification tool for cryptocurrencies in the future.

It is a challenging task to recognize smoke from images due to large variance of smoke color, texture, and shapes. There are smoke detection methods that have been proposed, but most of them are based on hand-crafted features. To improve the performance of smoke detection, we propose a novel deep normalization and convolutional neural network (DNCNN) with 14 layers to implement automatic feature extraction and classification. In DNCNN, traditional convolutional layers are replaced with normalization and convolutional layers to accelerate the training process and boost the performance of smoke detection. To reduce overfitting caused by imbalanced and insufficient training samples, we generate more training samples from original training data sets by using a variety of data enhancement techniques. Experimental results show that our method achieved very low false alarm rates below 0.60% with detection rates above 96.37% on our smoke data sets.

The goal of single-image deraining is to restore the rain-free background scenes of an image degraded by rain streaks and rain accumulation. The early single-image deraining methods employ a cost function, where various priors are developed to represent the properties of rain and background layers. Since 2017, single-image deraining methods step into a deep-learning era, and exploit various types of networks, i.e., convolutional neural networks, recurrent neural networks, generative adversarial networks, etc., demonstrating impressive performance. Given the current rapid development, in this paper, we provide a comprehensive survey of deraining methods over the last decade. We summarize the rain appearance models, and discuss two categories of deraining approaches: model-based and data-driven approaches. For the former, we organize the literature based on their basic models and priors. For the latter, we discuss the developed ideas related to architectures, constraints, loss functions, and training datasets. We present milestones of single-image deraining methods, review a broad selection of previous works in different categories, and provide insights on the historical development route from the model-based to data-driven methods. We also summarize performance comparisons quantitatively and qualitatively. Beyond discussing the technicality of deraining methods, we also discuss the future possible directions.

The high volatility of the blockchain markets has driven the attention of investors and market participants to concentrate on the diversification avenues of NFTs, DeFi Tokens, and Cryptocurrencies. We examined the extreme risk transmission of blockchain markets using the quantile connectedness technique at the median, extreme low, and extreme high volatility conditions. We find significant risk spillovers among blockchain markets with strong disconnection of NFTs. Meanwhile, time-varying features characterized various uneven economic circumstances. Overall, NFTs offer greater diversification avenues with substantial risk-bearing potential among other blockchain markets to shelter the investments and minimize extreme risks.

This research paper aims to explore the role of FDI inflows and stock market development on the promotion of renewable energy consumption. Furthermore, study investigates the effect of renewable energy consumption on CO2 emissions and economic output across a panel of Brazil, China, India, and South Africa. Study utilizes annual data from 1990 to 2012 and employs various robust panel econometric techniques. The findings confirm that both FDI inflows and stock market development play an important role in promoting renewable energy consumption. The results also reveal that renewable energy consumption helps to mitigate the growth of CO2 emissions and promotes economic development.

Ordinary least-square (OLS) estimators for a linear model are very sensitive to unusual values in the design space or outliers among y values. Even one single atypical value may have a large effect on the parameter estimates. This article aims to review and describe some available and popular robust techniques, including some recent developed ones, and compare them in terms of breakdown point and efficiency. In addition, we also use a simulation study and a real data application to compare the performance of existing robust methods under different scenarios.