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In recent years, China's financial sector has gradually been alienated from the real sector, allowing financial innovation and regulatory arbitrage add their own value to finance. High interest rates in the financial industry have led to changes in the real sector, revealing a trend toward "financialization" and "quasi-financialization"; a typical example of this includes nonfinancial enterprises' shadow banking activities. In this article, we use annual data from 2004 to 2015 of A share listed companies on the Shanghai and Shenzhen Stock Exchanges, to examine the influence of nonfinancial enterprises' shadow banking activities on business performance. The results show that, overall, enterprises' shadow banking activity improve operating performance. In addition, from the perspective of earning structure, nonfinancial enterprises' shadow banking business increases financial benefits, but has a significantly negative effect on operating income. Further tests show that enterprises engaged in shadow banking activities will impact operating income through the two intermediary variables of investment scale and investment efficiency. However, the negative effect of investment in crowding out operating income is greater than that of the efficiency-improving effect on operating income. This article provides policy guidance in terms of recognizing diverse aspects of shadow banking system that divorce the real economy from the financial economy. [ABSTRACT FROM AUTHOR]

This study introduces a cutting-edge, high-resolution tool leveraging the predictive prowess of convolutional neural networks to advance the field of hazard assessment in urban pluvial flooding scenarios. The tool uniquely accounts for the high heterogeneity of urban space and the potential impact of complex climate scenarios, which are often underestimated by traditional data-reliant methods. Employing Shenzhen as a case study, the model showcased superior accuracy, resilience, and interpretability, illuminating potential flood hazards. The performance analysis shows that the model can accurately predict the vast majority of urban flood depths, but has errors in extreme flood predictions (depths greater than 35 cm). Findings underscore escalating flood impacts under enhanced scenario loads, with western and central Shenzhen—regions rife with construction—highlighted as particularly vulnerable. Under the most severe matrix scenario (Scenario 25), economic losses are estimated to be about $25,484 million. These commercial and residential hotspots are anticipated to suffer maximum economic loss, with these two areas accounting for 39.6% and 25.1% of the total losses, necessitating reinforced mitigation efforts, especially during extreme rainfall events and high soil saturation levels. In addition, the flooding control strategies should prioritize the reduction of flood inundation areas and integrate functionally oriented land use characteristics in their development. By aiding in the precise identification of flood-prone areas, this research expedites the development of efficient evacuation plans, bolsters urban sustainability, and augments climate resilience, ultimately mitigating flood-induced economic tolls. [Display omitted] • CNN-based tool accurately predicts urban flood hazards under matrix scenarios. • Model identifies western, central Shenzhen as highly flood-prone zones. • Model identifies economic impact on commercial, residential areas. • Function-oriented land use planning aids effective flood control strategies. [ABSTRACT FROM AUTHOR]

Service migration in mobile edge computing is a promising approach to improving the quality of service (QoS) for mobile users and reducing the network operational cost for service providers as well. However, these benefits are not free, coming at costs of bulk-data transfer, and likely service disruption, which could consequently increase the overall service costs. To gain the benefits of service migration while minimizing its cost across the edge nodes, in this paper, we leverage reinforcement learning (RL) method to design a cost-effective framework, called Mig-RL , for the service migration with a reduction of total service costs as a goal in a mobile edge environment. The Mig-RL leverages the infrastructure of edge network and deploys a migration agent through Q-learning to learn the optimal policy with respect to the service migration status. We distinguish the Mig-RL from other existing works in several major aspects. First, we fully exploit the nature of this problem in a modest migration space, which allows us to constrain the number of service replicas whereby a defined state–action space could be effectively handled, as opposed to those methods that need to always approximate a huge state–action space for policy optimality. Second, we advocate a migration policy-base as a cache to save the learning process by retrieving the most effective policy whenever a similar migration pattern is encountered as time goes on. Finally, by exploiting the idea of software defined network, we also investigate the efficient implementation of Mig-RL in mobile edge network. Experimental results based on some real and synthesized access sequences show that Mig-RL , compared with the selected existing algorithms, can substantially minimize the service costs, and in the meantime, efficiently improve the QoS by adapting to the changes of mobile access patterns. • A Q-learning-based framework is proposed for service migration in mobile edges. • A policy-cache-based method is introduced to optimize the learning process of the framework. • A software-defined approach is presented to effectively implement the framework. • A prototype implementation is evaluated with the trace data gathered from Shenzhen taxi system and metro stations. [ABSTRACT FROM AUTHOR]

[Display omitted] • XGBoost-SHAP model assesses morphological impact on urban flooding susceptibility. • Unveils key factors affecting flooding - urban form, rainfall, and topography. • Building form parameters show significant influence on flood depth. • Optimal intervals identified for main factors to improve stormwater management. Urban flooding risks, often overlooked by conventional methods, can be profoundly affected by city configurations. However, explainable Artificial Intelligence could provide insights into how urban configurations affected urban flooding. This study, taking entered on Shenzhen City, deploys an XGBoost, integrating SHapley Additive exPlanation and Partial Dependency Plots, to assess how urban morphology influences urban flooding susceptibility. The models and strategies presented in this study aimed to adapt to extreme storms from the perspective of city spatial configuration planning. The findings underscore the varying impact of disaster variables on urban flooding, with morphological attributes becoming highly significant during severe inundations. In the analysis, mean building volume emerged as a pivotal parameter, with a mean SHAP value of 0.0107 m and a contribution ratio of 9.70 %. The study indicates that building configurations should be optimized to minimize urban flooding risks. It is recommended that the Mean Building Volume (MBV) be maintained within the range of 1.25 km3 to 2.5 km3, and the Standard Deviation of Building Volume (SDBV) be kept below 2.814 km3. By harnessing explainable Artificial Intelligence algorithms, this study offers insights into the intricate relationship between urban forms and flood risk, thereby informing the development of effective urban flooding adaptation strategies. [ABSTRACT FROM AUTHOR]