Zhongnan University of Economics and Law

The advances in information technology have witnessed great progress on healthcare technologies in various domains nowadays. However, these new technologies have also made healthcare data not only much bigger but also much more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, the characteristics of these data are that they are stored in different formats and created quickly, which can, to a large extent, be regarded as a big data problem. To provide a more convenient service and environment of healthcare, this paper proposes a cyber-physical system for patient-centric healthcare applications and services, called Health-CPS, built on cloud and big data analytics technologies. This system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.

Rapid urban expansion has profound impacts on global biodiversity through habitat conversion, degradation, fragmentation, and species extinction. However, how future urban expansion will affect global biodiversity needs to be better understood. We contribute to filling this knowledge gap by combining spatially explicit projections of urban expansion under shared socioeconomic pathways (SSPs) with datasets on habitat and terrestrial biodiversity (amphibians, mammals, and birds). Overall, future urban expansion will lead to 11-33 million hectares of natural habitat loss by 2100 under the SSP scenarios and will disproportionately cause large natural habitat fragmentation. The urban expansion within the current key biodiversity priority areas is projected to be higher (e.g., 37-44% higher in the WWF's Global 200) than the global average. Moreover, the urban land conversion will reduce local within-site species richness by 34% and species abundance by 52% per 1 km grid cell, and 7-9 species may be lost per 10 km cell. Our study suggests an urgent need to develop a sustainable urban development pathway to balance urban expansion and biodiversity conservation.

The phenomenon of copycats is common in a wide range of industries. Recently, to indicate product authenticity and combat copycats, many brand name companies (BNCs) have started selling products through retailers. These BNCs deploy a scalable protocol that is integrated into a permissioned blockchain technology (PBT) platform. We examine how PBT combats copycats in the supply chain and how it benefits BNCs. Although PBT implementation helps novice customers identify product authenticity and the real quality of products, that is, to take advantage of a quality disclosure effect , we show that, if and only if the number of novice customers is large enough, then selling through a PBT retailer can effectively combat copycats. Thus, PBT increases the profit of the BNC, consumer surplus, social welfare, and reduces the profit of a copycat. Moreover, conventional wisdom tells us that PBT ensures supply chain transparency and motivates a firm to improve its product quality. However, the BNC reduces the quality of its products when using PBT, because an improvement in product quality is not profitable if consumers can distinguish between genuine and imitation products. Furthermore, we extend the model by considering the case where the BNC itself implements PBT. Without the double marginalization effect , even if the number of novice customers is small, blockchain technology may exist in the market (the BNC self‐implements). In addition, if the unit production cost of a genuine product is large enough, social welfare increases when production cost increases.

At present, computed tomography (CT) is widely used to assist disease diagnosis. Especially, computer aided diagnosis (CAD) based on artificial intelligence (AI) recently exhibits its importance in intelligent healthcare. However, it is a great challenge to establish an adequate labeled dataset for CT analysis assistance, due to the privacy and security issues. Therefore, this paper proposes a convolutional autoencoder deep learning framework to support unsupervised image features learning for lung nodule through unlabeled data, which only needs a small amount of labeled data for efficient feature learning. Through comprehensive experiments, it shows that the proposed scheme is superior to other approaches, which effectively solves the intrinsic labor-intensive problem during artificial image labeling. Moreover, it verifies that the proposed convolutional autoencoder approach can be extended for similarity measurement of lung nodules images. Especially, the features extracted through unsupervised learning are also applicable in other related scenarios.

An intelligent transportation system (ITS) plays an important role in public transport management, security and other issues. Traffic flow detection is an important part of the ITS. Based on the real-time acquisition of urban road traffic flow information, an ITS provides intelligent guidance for relieving traffic jams and reducing environmental pollution. The traffic flow detection in an ITS usually adopts the cloud computing mode. The edge of the network will transmit all the captured video to the cloud computing center. However, the increasing traffic monitoring has brought great challenges to the storage, communication and processing of traditional transportation systems based on cloud computing. To address this issue, a traffic flow detection scheme based on deep learning on the edge node is proposed in this article. First, we propose a vehicle detection algorithm based on the YOLOv3 (You Only Look Once) model trained with a great volume of traffic data. We pruned the model to ensure its efficiency on the edge equipment. After that, the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm is optimized by retraining the feature extractor for multiobject vehicle tracking. Then, we propose a real-time vehicle tracking counter for vehicles that combines the vehicle detection and vehicle tracking algorithms to realize the detection of traffic flow. Finally, the vehicle detection network and multiple-object tracking network are migrated and deployed on the edge device Jetson TX2 platform, and we verify the correctness and efficiency of our framework. The test results indicate that our model can efficiently detect the traffic flow with an average processing speed of 37.9 FPS (frames per second) and an average accuracy of 92.0% on the edge device.

Historically, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) was the unique satellite sensor used to collect the nighttime light, which is an efficient means to map the global economic activities. Since it was launched in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite has become a new satellite used to monitor nighttime light. This study performed the first evaluation on the NPP-VIIRS nighttime light imagery in modeling economy, analyzing 31 provincial regions and 393 county regions in China. For each region, the total nighttime light (TNL) and gross regional product (GRP) around the year of 2010 were derived, and a linear regression model was applied on the data. Through the regression, the TNL from NPP-VIIRS were found to exhibit R2 values of 0.8699 and 0.8544 with the provincial GRP and county GRP, respectively, which are significantly stronger than the relationship between the TNL from DMSP-OLS (F16 and F18 satellites) and GRP. Using the regression models, the GRP was predicted from the TNL for each region, and we found that the NPP-VIIRS data is more predictable for the GRP than those of the DMSP-OLS data. This study demonstrates that the recently released NPP-VIIRS nighttime light imagery has a stronger capacity in modeling regional economy than those of the DMSP-OLS data. These findings provide a foundation to model the global and regional economy with the recently availability of the NPP-VIIRS data, especially in the regions where economic census data is difficult to access.

This study investigated the influence of service robot attributes on customers’ hospitality experience from the perspective of relationship building. Through literature review and a preliminary study with in-depth interviews, a conceptual framework was developed. A scenario-based experiment and questionnaire survey were designed to test the model. The results indicate that robots’ being perceived as humanlike or intelligent positively affects customer-robot rapport building and the hospitality experience. Additionally, customer-employee rapport building was found to mediate the relationship between robot attributes and the hospitality experience, but customer-robot rapport building was not. Based on these findings, theoretical contributions and practical implications were discussed.

With the rapid development of the Internet of Things, cloud computing, and big data, more comprehensive and powerful applications become available. Meanwhile, people pay more attention to higher QoE and QoS in a “terminal- cloud” integrated system. Specifically, both advanced terminal technologies (e.g., smart clothing) and advanced cloud technologies (e.g., big data analytics and cognitive computing in clouds) are expected to provide people with more reliable and intelligent services. Therefore, in this article we propose a Wearable 2.0 healthcare system to improve QoE and QoS of the next generation healthcare system. In the proposed system, washable smart clothing, which consists of sensors, electrodes, and wires, is the critical component to collect users' physiological data and receive the analysis results of users' health and emotional status provided by cloud-based machine intelligence.

Blockchain technology has a wide range of applications in the fields of finance, credit reporting and intellectual property, etc. As the core of blockchain, consensus algorithm affects the security and performance of blockchain system directly. In the past 10 years, there have been about 30 consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Ripple Protocol Consensus Algorithm (RPCA) and AlgoRand. But their security, stability and operating efficiency still lag far behind our actual needs. This paper introduces the computing power competition of PoW into DPoS to design an improved consensus algorithm named Delegated Proof of Stake with Downgrade (DDPoS). Through the further modification, the impact of both computing resources and stakes on generating blocks is reduced to achieve higher efficiency, fairness, and decentralization in consensus process. Then a downgrade mechanism is proposed to quickly replace the malicious nodes to improve the security. The simulation experiments in blockchain system show that the proposed consensus algorithm is significantly more efficient than PoW and PoS, but slightly lower than DPoS. However, its degree of centralization remains far below that of DPoS. And through the downgrade mechanism, the proposed consensus algorithm can detect and downgrade the malicious nodes timely to ensure the security and good operation of system.

ABSTRACT We examine the improvements in forecast accuracy that result from analysts' visits to listed companies. We find that company visits significantly enhance the accuracy of the analysts' earnings forecasts for those companies. The benefit from company visits is more pronounced for companies that are more neglected or less accessible and for brokerage firms that face less pressure for optimistic forecasts from buy‐side clients. Our results are robust and remain significant after controlling for endogeneity and selection bias. Overall, our findings show that private interactions with company management provide analysts with an informational advantage and suggest that company visits facilitate the mosaic approach to information acquisition.

Abstract Multilingual crisis communication has emerged as a global challenge during the COVID-19 pandemic. Global public health communication is characterized by the large-scale exclusion of linguistic minorities from timely high-quality information. The severe limitations of multilingual crisis communication that the COVID-19 crisis has laid bare result from the dominance of English-centric global mass communication; the longstanding devaluation of minoritized languages; and the failure to consider the importance of multilingual repertoires for building trust and resilient communities. These challenges, along with possible solutions, are explored in greater detail by the articles brought together in this special issue, which present case studies from China and the global Chinese diaspora. As such, the special issue constitutes not only an exploration of the sociolinguistics of the COVID-19 crisis but also a concerted effort to open a space for intercultural dialogue within sociolinguistics. We close by contending that, in order to learn lessons from COVID-19 and to be better prepared for future crises, sociolinguistics needs to include local knowledges and grassroots practices not only as objects of investigation but in its epistemologies; needs to diversify its knowledge base and the academic voices producing that knowledge base; and needs to re-enter dialogue with policy makers and activists.

Taking the cities along the Yellow River Basin (YRB) as research objects, the entropy-TOPSIS model is used to evaluate urban ecological level; and the main obstacle factors that restrict the improvement of urban ecological level are analyzed through the obstacle diagnosis model. The results show that ecological level of the cities along the YRB has been steadily increasing, and there is a significant correlation with the city size and watershed location, and the gap between cities has declined; the spatial pattern is characterized by “high in head and tail, low in center”. The number of full-time teachers in colleges, the number of college students, living area per capita, and garden space per capita are the main obstacle factors for most cities; the environmental foundation, infrastructure, public services, economic potential, and innovation vitality are the main obstacle factors for the criterion layer. In the future, we should adhere to the concept of basin-based governance, make full use of a series of strategic overlapping effects such as the development of urban agglomerations, the strategy of strengthening the transportation nation, and the construction of ecological civilization, relying on strategies such as green economic transformation, industrial structure upgrade, and ecological corridor construction to build the YRB's ecological economic culture and to create high-quality ecological cities.

This study aims to investigate the impact of COVID-19 pandemic on the stock markets of sixteen countries. Pooled OLS regression, conventional t-test and Mann-Whitney test are used to estimate the results of the study. We construct a weekly panel data of COVID-19 new cases and stock returns. Pooled OLS estimation result shows that the growth rate of weekly new cases of COVID-19 negatively predicts the return in stock market. Next, the returns on leading stock indices of these countries during the COVID-19 outbreak period are compared with returns during the non-COVID period. We use a t-test and Mann-Whitney test to compare the returns. The results reveal that investors in these countries do not react to the media news of COVID-19 at the early stage of the pandemic. However, once the human-to-human transmissibility had been confirmed, all of the stock market indices negatively reacted to the news in the short- and long-event window. Interestingly, we noticed that the Shanghai Composite Index, which was severely affected during the short-event window, bounced back during the long-event window. This indicates that the Chinese government's drastic measures to contain the spread of the pandemic regained the confidence of investors in the Shanghai Stock Market.

Diabetic foot ulcers (DFUs) are associated with complex pathogenic factors and are considered a serious complication of diabetes. The potential mechanisms underlying DFUs have been increasingly investigated. Previous studies have focused on the three aspects of diabetic peripheral vascular disease, neuropathy, and wound infections. With advances in technology, researchers have been gradually conducting studies using immune cells, endothelial cells, keratinocytes, and fibroblasts, as they are involved in wound healing. It has been reported that the upregulation or downregulation of molecular signaling pathways is essential for the healing of DFUs. With a recent increase in the awareness of epigenetics, its regulatory role in wound healing has become a much sought-after trend in the treatment of DFUs. This review focuses on four aspects involved in the pathogenesis of DFUs: physiological and pathological mechanisms, cellular mechanisms, molecular signaling pathway mechanisms, and epigenetics. Given the challenge in the treatment of DFUs, we are hopeful that our review will provide new ideas for peers.

Although there is a growing interest toward the topic of leader humility, extant research has largely failed to consider the underlying mechanisms through which leader humility influences team outcomes. In this research, we integrate the emerging literature of leader humility and social information processing theory to theorize how leader humility facilitates the development of collective team psychological capital, leading to higher team task allocation effectiveness and team performance. While Owens and Hekman (2016) suggest that leader humility has homogeneous effects on followers, we propose a potential heterogeneous effect based on the complementarity literature (e.g., Tiedens, Unzueta, & Young, 2007) and the principle of equifinality (leaders may influence team outcomes through multiple pathways; Morgeson, DeRue, & Karam, 2010). In three studies conducted in China, Singapore, and Portugal, including an experiment, a multisource field study, and a three-wave multisource field study, we find support for our hypotheses that leader humility enhances team performance serially through increased team psychological capital and team task allocation effectiveness. We discuss the theoretical implications of our work to the leader humility, psychological capital, and team effectiveness literatures; and offer suggestions for future research.

Abstract Using panel data from 37 countries from 1990 to 2019 and applying a quantile regression approach with panel fixed effects, we investigate the impact of national Environmental, Social, and Governance (ESG) performance on green innovation and how this impact varies across different green innovation capacity distributions. The research conclusions are as follows. (1) National ESG performance improvement significantly promotes green innovation. (2) The improvement of environmental performance and governance performance significantly promotes green innovation, but in countries with weak green innovation capabilities, the improvement of social performance reduces the output of green innovation. (3) The role of national ESG performance in promoting green innovation is stronger in countries with weak green innovation capabilities. (4) In non‐high‐income countries, the stronger the green innovation capability is, the more obvious is the promotion of ESG performance to green innovation. The findings of this paper provide empirical evidence and a policy basis for governments to focus on improving ESG performance and to commit to promoting green innovation activities more effectively.

The sixth-generation network (6G) is expected to achieve a fully connected world, which makes full use of a large amount of sensitive data. Federated Learning (FL) is an emerging distributed computing paradigm. In Vehicular Edge Computing (VEC), FL is used to protect consumer data privacy. However, using FL in VEC will lead to expensive communication overheads, thereby occupying regular communication resources. In the traditional FL, the massive communication rounds before convergence lead to enormous communication costs. Furthermore, in each communication round, many clients upload large quantity model parameters to the parameter server in the uplink communication phase, which increases communication overheads. Moreover, a few straggler links and clients may prolong training time in each round, which will decrease the efficiency of FL and potentially increase the communication costs. In this work, we propose an efficient-communication approach, which consists of three parts, including “Customized”, “Partial”, and “Flexible”, known as FedCPF. FedCPF provides a customized local training strategy for vehicular clients to achieve convergence quickly through a constraint item within fewer communication rounds. Moreover, considering the uplink congestion, we introduce a partial client participation rule to avoid numerous vehicles uploading their updates simultaneously. Besides, regarding the diverse finishing time points of federated training, we present a flexible aggregation policy for valid updates by constraining the upload time. Experimental results show that FedCPF outperforms the traditional FedAVG algorithm in terms of testing accuracy and communication optimization in various FL settings. Compared with the baseline, FedCPF achieves efficient communication with faster convergence speed and improves test accuracy by 6.31% on average. In addition, the average communication optimization rate is improved by 2.15 times.

Currently, advanced communications and networks greatly enhance user experiences and have a major impact on all aspects of people's lifestyles in terms of work, society, and the economy. However improving competitiveness and sustainable vehicle network services, such as higher user experience, considerable resource utilization and effective personalized services, is a great challenge. Addressing these issues, this paper proposes a virtual network resource management based on user behavior to further optimize the existing vehicle communications. In particular, ensemble learning is implemented in the proposed scheme to predict the user's voice call duration and traffic usage for supporting user-centric mobile services optimization. Sufficient experiments show that the proposed scheme can significantly improve the quality of services and experiences and that it provides a novel idea for optimizing vehicle networks.

Abstract Incorporating instrumental and political views of corporate social responsibility (CSR), this study examines the relationship between CSR and corporate financial performance in China's unique institutional context, which is featured by the coexistence of a strong government and a transitional market economy. Our results show that (a) CSR positively affects financial performance, (b) state ownership weakens the relationship between CSR and financial performance, and (c) industry competition strengthens the relationship between CSR and financial performance for both state‐owned and non‐state‐owned firms. This study reveals that, although both an instrumental view and a political view of CSR are applicable in China, the motivation to create economic benefits for firms dominates, and market competition increases the strategic use of CSR.

Motivated by Industry 4.0, 5G-enabled unmanned aerial vehicles (UAVs; also known as drones) are widely applied in various industries. However, the open nature of 5G networks threatens the safe sharing of data. In particular, privacy leakage can lead to serious losses for users. As a new machine learning paradigm, federated learning (FL) avoids privacy leakage by allowing data models to be shared instead of raw data. Unfortunately, the traditional FL framework is strongly dependent on a centralized aggregation server, which will cause the system to crash if the server is compromised. Unauthorized participants may launch poisoning attacks, thereby reducing the usability of models. In addition, communication barriers hinder collaboration among a large number of cross-domain devices for learning. To address the abovementioned issues, a blockchain-empowered decentralized horizontal FL framework is proposed. The authentication of cross-domain UAVs is accomplished through multisignature smart contracts. Global model updates are computed by using these smart contracts instead of a centralized server. Extensive experimental results show that the proposed scheme achieves high efficiency of cross-domain authentication and good accuracy.