Digital transformation and corporate financialization in emerging markets: Evidence from China.

Corporate financialization poses serious challenges to the development of the real economy. In the context of promoting the deep integration of the digital economy and the real economy, it is crucial to explore whether digital transformation can inhibit corporate financialization. Using data from Chinese listed companies from 2009 to 2021, we construct a fixed effects model and find that digital transformation significantly reduces the level of corporate financialization, a conclusion that still holds after a series of robustness tests such as propensity score matching and adding control variables.

Application of the extended value-belief-norm (VBN) theory to understand consumers' intention to use autonomous delivery vehicles (ADVs).

Emissions from transportation fuel vehicles are polluting the environment more than ever before. Currently, autonomous delivery vehicles (ADVs) are welcomed by logistics service providers and lots of consumers in general for their significant contribution in reducing environmental pollution and increasing delivery efficiency. However, there is a lack of in-depth research on consumers' willingness to adopt ADVs, despite the fact that ADVs are innovative delivery method in the field of last-mile delivery.

Research on the dissemination of celebrities' opinions based on speech act theory and potential category analysis.

With the increasingly prominent role of social media in the timeliness and sharing of information dissemination, more and more research has focused on how to further improve user stickiness through social media. However, there is little consideration of the impact of celebrities' views on user behavior in social media. The main goal of this paper is to study the influence of celebrity language style on user communication and opinions dissemination.

A Novel Credible Carbon Footprint Traceability System for Low Carbon Economy Using Blockchain Technology.

To achieve the goal of carbon neutrality, many countries have established regional carbon emission trading markets and tried to build a low-carbon economic system. At present, the implementation of carbon emission trading and low-carbon economic systems faces many challenges such as manipulation, corruption, opacity, lack of trust, and lack of data tracking means. The application of blockchain technology can perfectly solve the above problems.

Exploring a Multi-Layer Coupled Network Propagation Model Based on Information Diffusion and Bounded Trust.

To explore the law of opinion dissemination and individual opinion evolution at the micro level, this paper analyzes the influence of variation and oyster on communication from the perspective of network structure. In this paper, we introduce the concepts of "variation" and "oyster", build a multi-layer coupled network environment combined with the ISOVR model, and conduct simulation experiments of network information dissemination based on the bounded trust model. The experimental results reveal that the extent and scope of variation's spread in the network are more dependent on the trust of nodes themselves, and decreasing the trust of nodes significantly reduces the rate and peak value of variation.

Visualization and Bibliometric Analysis of Carbon Neutrality Research for Global Health.

The visual analysis of carbon neutrality research can help better understand the development of the research field and explore the difficulties and hot spots in the research, thus making contributions to "carbon emission reduction," environmental protection and human health. This paper makes a visual quantitative analysis of 2,819 research papers published in top international journals from 2008 to 2021 in the WOS core database. It is found that China, the United States, Britain, and Germany are leading the way in carbon neutrality research.

Sharing Reward Program Based on Face Consciousness in Social Media.

Purpose: In social marketing, sharing reward program (SRP) is a common way to improve the marketing effect. However, few studies have explored the impact of consumers' self-presentation and face consciousness on enterprise SRP. This study aims to explore the influence of these two factors on the optimal SRP.

Exploring an Efficient Evolutionary Game Model for the Government-Enterprise-Public during the Double Carbon Policy in China.

The realization of China's "double carbon" goal is of great significance to the world environment and China's economy and society. Through the establishment of the "government-enterprise-public" evolutionary game model, this paper explores the interaction between government policy guidance, low-carbon technology R&D behavior of enterprises, and public purchase of carbon label products, as well as the micro-driving path, aiming to provide suggestions for the implementation of the "double carbon" policy and carbon label system in China. The results show that the choice of government, enterprises, and public strategies is closely related to their own costs and benefits.

Social Relationship Prediction Integrating Personality Traits and Asymmetric Interactions.

Social networks have become an important way for users to find friends and expand their social circle. Social networks can improve users' experience by recommending more suitable friends to them. The key lies in improving the accuracy of link prediction, which is also the main research issue of this study.

Online Rumor Diffusion Model Based on Variation and Silence Phenomenon in the Context of COVID-19.

In the era of mobile internet, information dissemination has made a new leap in speed and in breadth. With the outbreak of the coronavirus disease 2019 (COVID-19), the COVID-19 rumor diffusion that is not limited by time and by space often becomes extremely complex and fickle. It is also normal that a piece of unsubstantiated news about COVID-19 could develop to many versions.

Prevalence, risk factors and clinical characteristics of renal dysfunction in Chinese outpatients with growth simple renal cysts.

Background: Researchers have proved that simple renal cysts (SRCs) might be correlated with renal dysfunction, but it is still controversial. Thus, we conducted clinical research study with large sample size and long-term follow-up to clarify the relationship between SRCs and renal dysfunction.

Methods: A total of 571 SRCs patients in outpatients of nephrology department were included, we investigated the clinical characteristics of growth SRCs compared with non-growth SRCs, evaluated the incidence of renal dysfunction in SRCs and explored the risk factors of renal dysfunction in growth SRCs.

Doctor Recommendation Model Based on Ontology Characteristics and Disease Text Mining Perspective.

Background: Patients can access medical services such as disease diagnosis online, medical treatment guidance, and medication guidance that are provided by doctors from all over the country at home. Due to the complexity of scenarios applying medical services online and the necessity of professionalism of knowledge, the traditional recommendation methods in the medical field are confronting with problems such as low computational efficiency and poor effectiveness. At the same time, patients consulting online come from all sides, and most of them suffer from nonacute or malignant diseases, and hence, there may be offline medical treatment.

Influencing Factors of Continuous Use of Web-Based Diagnosis and Treatment by Patients With Diabetes: Model Development and Data Analysis.

Background: The internet has become a major source of health care information for patients and has enabled them to obtain continuous diagnosis and treatment services. However, the quality of web-based health care information is mixed, which raises concerns about the credibility of physician advice obtained on the internet and markedly affects patients' choices and decision-making behavior with regard to web-based diagnosis and treatment. Therefore, it is important to identify the influencing factors of continuous use of web-based diagnosis and treatment from the perspective of trust.

Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network.

Although the Crowd-Sensing perception system brings great data value to people through the release and analysis of high-dimensional perception data, it causes great hidden danger to the privacy of participants in the meantime. Currently, various privacy protection methods based on differential privacy have been proposed, but most of them cannot simultaneously solve the complex attribute association problem between high-dimensional perception data and the privacy threat problems from untrustworthy servers. To address this problem, we put forward a local privacy protection based on Bayes network for high-dimensional perceptual data in this paper.

Optimized method to harvest both kidneys from one donor rat for transplantation.

Background: Rat renal transplantation is an essential experimental model for studies of transplantation immunobiology. Harvesting both kidneys from one donor rat for transplantation is widely used to reduce the number of experimental animals. Using the conventional method, both kidneys of the donor rat are harvested simultaneously, which leads to the prolonged warm ischemic times during transplantation of the second donor kidney.

Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection.

In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution.

A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm.

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means.