A New Algorithm for Detecting Local Community Based on Random Walk

This paper presents one new algorithm for local community discovery. It employs a new vertex selection strategy which considers not only the boundary structure of candidate local community but also the probability which the investigated vertex will return to the candidate local community. A local random walk is adopted to compute this return probability which does not require the global information. We choose four algorithms for comparison which are the best ones existed by far. For better evaluation, the datasets include not only the computer generated graphs in standard benchmark but also the real-world networks which are classical ones in global community discovery. The experimental results show our algorithm outperforms the other ones on the computer generated graphs. The performance of our algorithm is approximately the same with the algorithm proposed by Luo, Wang and Promislow on real-world networks

Study on dynamic characteristics’ change of hippocampal neuron reduced models caused by the Alzheimer’s disease

In the paper, based on the electrophysiological experimental data, the Hippocampal neuron reduced model under the pathology condition of Alzheimer’s disease (AD) has been built by modifying parameters’ values. The reduced neuron model’s dynamic characteristics under effect of AD are comparatively studied. Under direct current stimulation, compared with the normal neuron model, the AD neuron model’s dynamic characteristics have obviously been changed. The neuron model under the AD condition undergoes supercritical Andronov–Hopf bifurcation from the rest state to the continuous discharge state. It is different from the neuron model under the normal condition, which undergoes saddle-node bifurcation. So, the neuron model changes into a resonator with monostable state from an integrator with bistable state under AD’s action. The research reveals the neuron model’s dynamic characteristics’ changing under effect of AD, and provides some theoretic basis for AD research by neurodynamics theory

Short-term effect of a developed warming moist chamber goggle for video display terminal-associated dry eye

Abstract Background Video display terminal (VDT)-associated dry eye (DE) patients are the rising group worldwide, and moisture goggles are the preferable treatment since they are capable of improving tear film stability and DE discomfort. The current study aims to evaluate the short-term efficacy and safety of the developed warming moist chamber goggles (WMCGs) for VDT-associated DE patients. Methods In this prospective self-control study, 22 DE patients (22 eyes) working with VDTs over 4 h daily were enrolled and instructed to wear WMCGs for 15 min. Sodium hyaluronate (SH, 0.1%) eyedrops were applied as a control on another day on these same patients, however 4 subjects denied the eyedrop application. The symptomatology visual analog scale (VAS) score, tear meniscus height (TMH), noninvasive tear film break-up time (NI-BUT), tear film lipid layer thickness (LLT), and bulbar conjunctival redness were assessed with Keratograph 5 M at baseline, 5, 30 and 60 min after treatment. The WMCGs wearing comfort was also evaluated. Results The ocular discomfort evaluated by VAS decreased in the WMCGs group throughout 60 min (P<0.001), better than the control group levels (P ≤ 0.015). TMH, NI-BUT (including the first BUT and average BUT) increased than baseline level accross 60 min in the WMCG group (P ≤ 0.012), while those in the control group only showed temporary improvements in 5 min. LLT also increased obviously after WMCGs wear, while the change in the control group was nearly innoticeable. No adverse responses were detected. Conclusions Temporary use of the WMCGs is able to relieve ocular discomfort, and improves tear film stability in DE patients for at least 1 h, making it a promising alternative to other treatments

Descriptive statistics.

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy.</div

The regression results of PECM II.

Innovation drive differs from investment drive and resource drive in that it focuses on knowledge and skills to promote productivity growth. By integrating technical standards within the framework of an innovation-driven development system in this work, theoretical implications for this development strategy may be revealed. Following our theoretical study, we built a PECM utilizing China’s inter-provincial panel data from 2007 to 2020 to investigate the long and short-term relationships between standardization, R&D, and innovation-driven development. The following are the key findings: First, both standardization and R&D are the nation’s critical engines of innovation-driven development. Second, standardization has the greatest impact on TFP through improving technical efficiency, whereas R&D drives both technical development and technical efficiency improvement. Third, while the influence of technical standard drafters’ production scale on scale efficiency was insignificant from 2007 to 2013, it became substantial after 2014 with China’s macroeconomic reform of "transforming the mode and changing the structure."</div

Vif test.

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy.</div

The result of the robust test.

Innovation drive differs from investment drive and resource drive in that it focuses on knowledge and skills to promote productivity growth. By integrating technical standards within the framework of an innovation-driven development system in this work, theoretical implications for this development strategy may be revealed. Following our theoretical study, we built a PECM utilizing China’s inter-provincial panel data from 2007 to 2020 to investigate the long and short-term relationships between standardization, R&D, and innovation-driven development. The following are the key findings: First, both standardization and R&D are the nation’s critical engines of innovation-driven development. Second, standardization has the greatest impact on TFP through improving technical efficiency, whereas R&D drives both technical development and technical efficiency improvement. Third, while the influence of technical standard drafters’ production scale on scale efficiency was insignificant from 2007 to 2013, it became substantial after 2014 with China’s macroeconomic reform of "transforming the mode and changing the structure."</div

The quantile regression graph of the influence of digital economy on green total factor productivity.

Note: In the figure, the horizontal axis represents different quantile points of the digital economy’s impact on green total factor productivity, while the vertical axis represents the regression coefficients of the digital economy. The dashed line segments depict the OLS regression estimates of the digital economy, and the region between the two dotted lines represents the confidence interval of the OLS regression values (with a confidence level of 0.95). The solid line represents the quantile regression estimates of the digital economy, with the shaded area indicating the confidence interval of the quantile regression estimates (at a confidence level of 0.95).</p

Model consistency test.

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy.</div