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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. Channel analysis shows that that digital transformation inhibits corporate financialization by enhancing the information mobility and operational capability of corporations. In addition, this effect is more pronounced at higher levels of industry competition as well as marketization. Finally, we also find structural differences in the impact of digital transformation on corporate financialization. Our study explores the determinants of corporate financialization in terms of a firm's mode of operation and type of strategy, and the findings provide a theoretical basis for the active development of digital technologies in emerging markets that are undergoing economic transitions, as well as for guarding against the shift of the economy from the real to the virtual.

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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. First, it analyzes the language style characteristics of celebrities' opinions and conducts cross-influence analysis between celebrity language style characteristics and user communication characteristics. Based on speech act theory, this study studies the influence of different language styles of celebrity Microblog on users' communication behavior and then builds a potential category analysis model to subdivide the views of celebrities. The results show that (1) Positive expression is the most common language style element combination of celebrities, and it also shows the most effective communication effect. This shows that users like to see celebrities show an active and positive side to the outside world, can analyze external things, and express their own opinions on these contents; (2) The combination of positive emotion, external attention, and analysis can produce the best communication effect; (3) The emotion of celebrities' opinions will affect the communication emotion of users to a certain extent, and the communication of users will have the development trend of reducing positive emotions and increasing negative emotions. Therefore, positive guidance and the dissemination of positive energy are more needed on public social platforms to minimize or avoid the dissemination of negative emotions.

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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. However, the data recorded on a blockchain are often multi-type and heterogeneous, and users at different levels such as regulators, enterprises, and consumers have different requirements for data types and granularity. This requires a quick and trustworthy method for monitoring the carbon footprint of enterprises and products. In this paper, the carbon footprint traceability of enterprises and products is taken as an application scenario, and the distributed traceability concept of "traceability off the chain and verification on the chain" is adopted. By reconstructing the pointer of the file structure of the distributed storage, an interactive traceability structure supporting type filtering is constructed, which enables fast retrieval and locating of carbon emission data in the mixed data on the chain. The experimental results show that using the interactive traceability structure that supports type filtering for traceability not only releases the computing power of full nodes but also greatly improves the traceability efficiency of the long-span transaction chain. The proposed carbon footprint traceability system can rapidly trace and track data on an enterprise's and a product's carbon footprint, as well as meet the needs of users at all levels for traceability. It also offers more advantages when handling large amounts of data requests.

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Objective: 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. Methods: 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. Results: 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. Changing the silence coefficient of variation does not effectively change the direction of rumor propagation, which indicates that rumor has a strong propagation ability after mutation. Conclusion: The insights of this paper on the dissemination of public opinions include: 1) pay attention to people with high trust levels, such as opinion leaders; 2) clarify the misinformation in time to prevent further spread of rumors.

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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. The research hotspots are mainly divided into three dimensions: (1) biomass energy and the negative effects it might bring; (2) ways and methods of electrochemical reduction of carbon dioxide; (3) catalysts and catalytic environment. The research mainly went through the conceptual period of 1997-2007, the exploration period of bioenergy from 2008 to 2021, the criticized period of bioenergy sources from 2011 to 2013, and the carbon dioxide electroreduction period from 2013 to the present. In the future, the research direction of biomass energy is to find one kind of biomass energy source which can be stored in a low-carbon way, produced in large quantities at a low cost, and will not occupy forestland. The electrolysis of water to produce hydrogen and the synthesis of fuel with CO2 are two major research directions at present, whose aims are to find the suitable catalyst and environment for the reaction. Besides, more research can be done on "carbon neutrality" policies so as to reduce carbon dioxide emissions from the source, develop a low-carbon economy and protect human health.

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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. Methods: A Stackelberg game between enterprises, sharers and potential consumers is developed to study the impact of sharers' face consciousness on enterprise's SRP. In order to discuss the impact of face consciousness on SRP in detail, we introduced status identity of commodity information, sharer's self-presentation preference and commodity price as exogenous variables in the research. Results: The results have shown that when the face consciousness of sharers is high, enterprises are advised to adopt the strategy of low reward and low requirement. But when the face consciousness is low, it would be better for them adopt the strategy of high reward and high requirement. In addition, with the low face consciousness, the optimal SRP is also affected by the relationship between the price of goods and the number of WeChat friends of sharers. Conclusion: The results suggest that when enterprises make incentive policies, considering consumers' self-presentation preference and face consciousness, the profit level can be effectively improved.

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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. Public sentiment can effectively urge the government to actively fulfill its responsibilities. Effective government policy guidance plays a key role in low-carbon technology R&D behavior of enterprises. There is an interaction between low-carbon technology R&D behavior of enterprises and public purchase of carbon label products.

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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. In the study of personality traits, some scholars have proved that personality can be used to predict users' behavior in social networks. Based on these studies, this study aims to improve the accuracy of link prediction in directed social networks. Considering the integration of personality link preference and asymmetric interaction into the link prediction model of social networks, a four-dimensional link prediction model is proposed. Through comparative experiments, it is proved that the four-dimensional social relationship prediction model proposed in this study is more accurate than the model only based on similarity. At the same time, it is also verified that the matching degree of personality link preference and asymmetric interaction intensity in the model can help improve the accuracy of link prediction.

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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. RESULTS: The mean baseline age was 51.31 ± 14.37 years in the whole cohort, ranging from 19 to 79 years, and 57.6% of them were male. The median follow-up duration was 3 years, ranging from 1 to 10 years. In addition, the final maximum diameter increased 1 mm (2.74%) per year. Patients in growth SRCs group tented to have higher percentage of hypertension, hematuria, large cyst and multiple cysts compared with non-growth SRCs group. The prevalence of renal dysfunction was 15.6% after the follow-up, and the prevalence of renal dysfunction was about 10 times higher in growth SRCs group than non-growth SRCs group (23.3% vs. 2.4%). Renal dysfunction was significantly associated with age, female, total cholesterol, diastolic blood pressure, final maximum diameter and yearly change in maximum diameter in growth SRCs. CONCLUSIONS: SRCs were closely related to the decline of renal function, we recommend close follow-up for growth SRCs.

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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. Therefore, this paper proposes an online prediagnosis doctor recommendation model by integrating ontology characteristics and disease text. Particularly, this recommendation model takes full consideration of geographical location of patients. OBJECTIVE: The recommendation model takes the real consultation data from online as the research object, fully testifying its effectiveness. Specifically, this model would make recommendation to patients on department and doctors based on patients' information of symptoms, diagnosis, and geographical location, as well as doctor's specialty and their department. METHODS: Utilizing crawler technique, five hospital departments were selected from the online medical service platform. The names of the departments were in accordance with the standardized department names used in real hospitals (e.g., endocrinology, dermatology, gynemetrics, pediatrics, and neurology). As a result, a dataset consisting of 20000 consultation questions by patients was built. Through the application of Python and MySQL algorithms, replacing semantic dictionary retrieval or word frequency statistics, word vectors were utilized to measure similarity between patients' prediagnosis and doctors' specialty, forming a recommendation framework on medical departments or doctors based on the above-obtained sentence similarity measurement and providing recommendation advices on intentional departments and doctors. RESULTS: In the online medical field, compared with the traditional recommendation method, the model proposed in the paper is of higher recommendation accuracy and feasibility in terms of department and doctor recommendation effectiveness. CONCLUSIONS: The proposed online prediagnosis doctor recommendation model integrates ontology characteristics and disease text mining. The model gives a relatively more accurate recommendation advice based on ontology characteristics such as patients' description texts and doctors' specialties. Furthermore, the model also gives full consideration on patients' location factors. As a result, the proposed online prediagnosis doctor recommendation model would improve patients' online consultation experience and offline treatment convenience, enriching the value of online prediagnosis data.

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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. We focus on the stagnant role and information variants in the process of rumor diffusion about COVID-19, and through the study of variability and silence in the dissemination, which combines the effects of stagnation phenomenon and information variation on the whole communication system in the circulation of rumors about COVID-19, based on the classic rumor SIR (Susceptible Infected Recovered) model, we introduce a new concept of "variation" and "oyster". The stability of the new model is analyzed by the mean field equation, and the threshold of COVID-19 rumor propagation is obtained later. According to the results of the simulation experiment, whether in the small world network or in the scale-free network, the increase of the immure and the silent probability of the variation can effectively reduce the speed of rumor diffusion about COVID-19 and is conducive to the dissemination of the truth in the whole population. Studies have also shown that increasing the silence rate of variation can reduce COVID-19 rumor transmission more quickly than the immunization rate. The interesting discovery is that at the same time, a higher rumor infection rate can bring more rumors about COVID-19 but does not always maintain a high number of the variation which could reduce variant tendency of rumors. The more information diffuses in the social group, the more consistent the version and content of the information will be, which proves that the more adequate each individual information is, the slower and less likely rumors about COVID-19 spread. This consequence tells us that the government needs to guide the public to the truth. Announcing the true information publicly could instantly contain the COVID-19 rumor diffusion well rather than making them hidden or voiceless.

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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. OBJECTIVE: The objective of our study was to investigate the influencing factors of patients' continuous use of web-based diagnosis and treatment based on the elaboration likelihood model and on trust theory in the face of a decline in physiological conditions and the lack of convenient long-term professional guidance. METHODS: Data on patients with diabetes in China who used an online health community twice or more from January 2018 to June 2019 were collected by developing a web crawler. A total of 2437 valid data records were obtained and then analyzed using correlation factor analysis and regression analysis to validate our research model and hypotheses. RESULTS: The timely response rate (under the central route), the reference group (under the peripheral route), and the number of thank-you letters and patients' ratings that measure physicians' electronic word of mouth are all positively related with the continuous use of web-based diagnosis and treatment by patients with diabetes. Moreover, the physician's professional title and hospital's ranking level had weak effects on the continuous use of web-based diagnosis and treatment by patients with diabetes, and the effect size of the physician's professional title was greater than that of the hospital's ranking level. CONCLUSIONS: From the patient's perspective, among all indicators that measure physicians' service quality, the effect size of a timely response rate is much greater than those of effect satisfaction and attitude satisfaction; thus, the former plays an essential role in influencing the patients' behavior of continuous use of web-based diagnosis and treatment services. In addition, the effect size of electronic word of mouth was greater than that of the physician's offline reputation. Physicians who provide web-based services should seek clues to patients' needs and preferences for receiving health information during web-based physician-patient interactions and make full use of their professionalism and service reliability to communicate effectively with patients. Furthermore, the platform should improve its electronic word of mouth mechanism to realize its full potential in trust transmission and motivation, ultimately promoting the patient's information-sharing behavior and continuous use of web-based diagnosis and treatment.

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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. This mechanism realizes the local data protection of the users at the very beginning, eliminates the possibility of other parties directly accessing the user's original data, and fundamentally protects the user's data privacy. During this process, after receiving the data of the user's local privacy protection, the perception server recognizes the dimensional correlation of the high-dimensional data based on the Bayes network, divides the high-dimensional data attribute set into multiple relatively independent low-dimensional attribute sets, and then sequentially synthesizes the new dataset. It can effectively retain the attribute dimension correlation of the original perception data, and ensure that the synthetic dataset and the original dataset have as similar statistical characteristics as possible. To verify its effectiveness, we conduct a multitude of simulation experiments. Results have shown that the synthetic data of this mechanism under the effective local privacy protection has relatively high data utility.

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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. Prolonged warm ischemia time is the main risk factor for delayed graft function. METHODS: Two different approaches are compared. Method 1, conventional method: both kidneys of the donor rat are harvested simultaneously and then transplanted into two recipients. During transplantation, the first and second donor kidneys were regarded as Group 1 and 2, respectively. Method 2, step-by-step method: after left nephrectomy, the donor rat survives, and we perform left renal transplantation (Group 3). Then, the right kidney of the surviving donor rat is incised and transplanted into the left side of the second recipient (Group 4). RESULTS: The success rates were 86.7, 93.3, 93.3 and 86.7% in groups 1, 2, 3 and 4, respectively. The warm ischemia times increased significantly in group 2 compared with the other 3 groups (p < 0.05) but differed non-significantly between groups 3 and 4 (p > 0.05). Serum creatinine levels, blood urea nitrogen and 24-h urine protein level obviously increased after kidney transplantation in group 2 compared with other groups (p < 0.05). CONCLUSIONS: We developed an optimized method for reducing warm ischemia time, thereby minimizing delayed graft function.

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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. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of k -nearest neighbor classification performance.

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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. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

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