The role of grid management in community risk governance: a case study in Yuelu, China

ObjectiveIn this study, we aim to provide a comprehensive analysis of the effectiveness of the risk prevention and control mechanism within the grid management model for community risk prevention. We emphasize the importance of thoroughly examining the risk prevention and control mechanism to enhance risk management efforts in urban communities, particularly in response to unforeseen outbreaks such as COVID-19.MethodsCase studies are widely acknowledged as one of the most effective approaches to examine governance in China. In this study, the “Yuelu Model” serves as an illustrative example to demonstrate the application and effectiveness of grid management in community risk governance. To ensure the validity of the case study, it is imperative to adhere to the principle of representativeness. The collection of case data involves a combination of primary and secondary sources, and supplementary information is obtained through follow-up investigations conducted via WeChat, telephone, and other means, thereby enhancing the comprehensiveness and accuracy of the data.ResultsOur analysis reveals significant findings regarding the impact of the grid management model, fulfilling a triple role as a “Social Safety Valve” in the management process: (1) Community stress reduction function, (2) Community alarm function, and (3) Community integration function. Furthermore, we explore the adaptability of the grid management mechanism in addressing community risks, highlighting its effectiveness and potential for broader application.DiscussionThe findings of this study suggest that: Firstly, it is crucial to establish a shared information repository among different departments on a big data platform. Secondly, a dynamic government public information internal network should be established through collaborative efforts among multiple departments. Thirdly, implementing a regular (or periodic) early warning mechanism is essential. Lastly, the establishment of a high-quality talent team for power grid management is highly recommended. Our research provides valuable insights to enhance community risk governance

Innovation Culture, R&D Intensity, and Firm Innovation

This paper aims to investigate the value of two types of innovation culture, namely employee-perceived and firm-proclaimed innovation culture. We quantify how employees perceive innovation culture by analyzing the text of 191542 employee reviews on Glassdoor and identifying the presence of firm-proclaimed innovation culture from their official websites. The results indicate that employee-perceived innovation culture has a positive influence on innovation output whereas firm-proclaimed innovation culture does not. Moreover, R&D intensity negatively moderates the effect of employee perceived innovation culture on firm innovation, such that the effect of employee perceived innovation culture is lower when R&D intensity is higher. This finding contradicts the observation of previous studies that used cross-sectional survey data. Nevertheless, our finding is consistent with the view that innovation culture cultivates the intrinsic motivation of employees, but the symbiotic control that comprises the increase of R&D intensity weakens it

Game-Theoretic Unlearnable Example Generator

Unlearnable example attacks are data poisoning attacks aiming to degrade the clean test accuracy of deep learning by adding imperceptible perturbations to the training samples, which can be formulated as a bi-level optimization problem. However, directly solving this optimization problem is intractable for deep neural networks. In this paper, we investigate unlearnable example attacks from a game-theoretic perspective, by formulating the attack as a nonzero sum Stackelberg game. First, the existence of game equilibria is proved under the normal setting and the adversarial training setting. It is shown that the game equilibrium gives the most powerful poison attack in that the victim has the lowest test accuracy among all networks within the same hypothesis space, when certain loss functions are used. Second, we propose a novel attack method, called the Game Unlearnable Example (GUE), which has three main gradients. (1) The poisons are obtained by directly solving the equilibrium of the Stackelberg game with a first-order algorithm. (2) We employ an autoencoder-like generative network model as the poison attacker. (3) A novel payoff function is introduced to evaluate the performance of the poison. Comprehensive experiments demonstrate that GUE can effectively poison the model in various scenarios. Furthermore, the GUE still works by using a relatively small percentage of the training data to train the generator, and the poison generator can generalize to unseen data well. Our implementation code can be found at https://github.com/hong-xian/gue

Data-Dependent Stability Analysis of Adversarial Training

Stability analysis is an essential aspect of studying the generalization ability of deep learning, as it involves deriving generalization bounds for stochastic gradient descent-based training algorithms. Adversarial training is the most widely used defense against adversarial example attacks. However, previous generalization bounds for adversarial training have not included information regarding the data distribution. In this paper, we fill this gap by providing generalization bounds for stochastic gradient descent-based adversarial training that incorporate data distribution information. We utilize the concepts of on-average stability and high-order approximate Lipschitz conditions to examine how changes in data distribution and adversarial budget can affect robust generalization gaps. Our derived generalization bounds for both convex and non-convex losses are at least as good as the uniform stability-based counterparts which do not include data distribution information. Furthermore, our findings demonstrate how distribution shifts from data poisoning attacks can impact robust generalization

Service Failure and Consumers’ Satisfaction with the Healthcare Industry: Moderating Role of Recommendation

This study explores the effects of service failure on different service attributes related to patients’ satisfaction (i.e., therapeutic effect and service attitude). We consider patients’ recommendation-seeking behavior and examine the moderating effects of recommendation before medical consultation and its differences between the online and offline word-of-mouth (WOM) recommendations. We collected over 3,000,000 reviews from a leading Chinese online health community to facilitate the empirical analysis. We use two ordinal logit models as bases and, find that service failure exerts a negative effect on patients’ both therapeutic effect satisfaction and service atti-tude satisfaction. Moreover, the effect of service fail-ure will be attenuated if patients seek recommenda-tions on doctors before consulting them. Moreover, the moderating effects of online WOM recommenda-tions is demonstrated to be lower than those of the offline ones. Our findings provide important perspectives for the literature and managerial suggestions for stakeholders