Jun Jason Zhang
Biographic Data
| ID | 9080617 |
|---|---|
| NAME | Jun Jason Zhang |
| GIVEN NAMES | Jun Jason |
| FAMILY NAME | Zhang |
| SIGNATURE | ZHANG J J |
| AFFILIATIONS | Wuhan University |
| ORCID | 0000-0001-6908-2671 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Political official turnover and environmental governance
Political factors are widely acknowledged determinants of local environmental outcomes, yet their precise influence on air pollution, particularly in the context of leadership transitions, remains a subject of ongoing debate without a clear consensus. This study addresses this gap by investigating the impact of changes within China’s dual local political system—the government and Party systems—on air quality in prefectural-level cities. Leveragin…
Financial asset allocation duality and enterprise upgrading
This study selects the financial data of Chinese non-financial listed companies from 2012 to 2021 as the research sample and empirically examines in detail the impact of financial asset allocation on enterprise upgrading and its mechanism. The study finds that financial assets have a dual influence on enterprise upgrading. Short-term financial assets provide the necessary funds for production activities, thus promoting enterprise upgrading. Long-…
HackGAN
Network alignment (NA) that identifies equivalent nodes across networks is an effective tool for integrating knowledge from multiple networks. The state-of-the-art NA methods learn inter-network node similarities based on labeled anchor links, which are costly, time-consuming, and difficult to acquire. Therefore, a few unsupervised network alignment (UNA) methods propose solving NA problems without anchor links. However, most existing UNA methods…
Explainable AI in Deep Reinforcement Learning Models for Power System Emergency Control
Artificial intelligence (AI) technology has become an important trend to support the analysis and control of complex and time-varying power systems. Although deep reinforcement learning (DRL) has been utilized in the power system field, most of these DRL models are regarded as black boxes, which are difficult to explain and cannot be used on occasions when human operators need to participate. Using the explainable AI (XAI) technology to explain w…
Prevalence and Influencing Factors of Depression Self-Management Among Chinese Community Residents
Objective: This study aimed to investigate the current status of depression self-management (DSM), and to identify influencing factors of DSM among Chinese community residents. Methods: Stratified random sampling methodology was adopted in this cross-sectional survey. Respondents completed a collection of self-administered questionnaires Results: The majority of participants were female (72.2%), having a mean age of 39 years (SD = 17.3). The tota…
Pedestrian Choice Modeling and Simulation of Staged Evacuation Strategies in Daya Bay Nuclear Power Plant
Considering the distances to exits, exits' capacities, the sizes of queues at exits, distances to the nuclear power plant, as well as individual characteristics, the exit choice model for pedestrians in the plume planning area is established based on a random forest model. This model is trained and verified with the survey data of residents around the Daya Bay Nuclear Power Plant collected from a serious game-based questionnaire system. Combining…
Characterizing the Propagation of Situational Information in Social Media During Covid-19 Epidemic
During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information pub…
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Pedestrian Choice Modeling and Simulation of Staged Evacuation Strategies in Daya Bay Nuclear Power Plant
Considering the distances to exits, exits' capacities, the sizes of queues at exits, distances to the nuclear power plant, as well as individual characteristics, the exit choice model for pedestrians in the plume planning area is established based on a random forest model. This model is trained and verified with the survey data of residents around the Daya Bay Nuclear Power Plant collected from a serious game-based questionnaire system. Combining…
Characterizing the Propagation of Situational Information in Social Media During Covid-19 Epidemic
During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information pub…
Prevalence and Influencing Factors of Depression Self-Management Among Chinese Community Residents
Objective: This study aimed to investigate the current status of depression self-management (DSM), and to identify influencing factors of DSM among Chinese community residents. Methods: Stratified random sampling methodology was adopted in this cross-sectional survey. Respondents completed a collection of self-administered questionnaires Results: The majority of participants were female (72.2%), having a mean age of 39 years (SD = 17.3). The tota…
Explainable AI in Deep Reinforcement Learning Models for Power System Emergency Control
Artificial intelligence (AI) technology has become an important trend to support the analysis and control of complex and time-varying power systems. Although deep reinforcement learning (DRL) has been utilized in the power system field, most of these DRL models are regarded as black boxes, which are difficult to explain and cannot be used on occasions when human operators need to participate. Using the explainable AI (XAI) technology to explain w…
Financial asset allocation duality and enterprise upgrading
This study selects the financial data of Chinese non-financial listed companies from 2012 to 2021 as the research sample and empirically examines in detail the impact of financial asset allocation on enterprise upgrading and its mechanism. The study finds that financial assets have a dual influence on enterprise upgrading. Short-term financial assets provide the necessary funds for production activities, thus promoting enterprise upgrading. Long-…
HackGAN
Network alignment (NA) that identifies equivalent nodes across networks is an effective tool for integrating knowledge from multiple networks. The state-of-the-art NA methods learn inter-network node similarities based on labeled anchor links, which are costly, time-consuming, and difficult to acquire. Therefore, a few unsupervised network alignment (UNA) methods propose solving NA problems without anchor links. However, most existing UNA methods…
Political official turnover and environmental governance
Political factors are widely acknowledged determinants of local environmental outcomes, yet their precise influence on air pollution, particularly in the context of leadership transitions, remains a subject of ongoing debate without a clear consensus. This study addresses this gap by investigating the impact of changes within China’s dual local political system—the government and Party systems—on air quality in prefectural-level cities. Leveragin…
Computer Science (4 works) · Artificial Intelligence (2 works) · Artificial neural network (2 works) · Business (2 works) · Complex Network Analysis Techniques (2 works) · Engineering (2 works) · Finance (2 works) · Medicine (2 works) · Political science (2 works) · Psychology (2 works)