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The Influence of Stock Ownership Structure on the Effectiveness of Internal Control
Yingnan Li,Shanyue Jin,Hyeongsop Shim 한국무역연구원 2021 무역연구 Vol.17 No.3
Purpose Improving the effectiveness of internal control has become the key to ensure the healthy development of China’s listed companies and safeguard their interests. Corporate governance and ownership structure will affect the implementation of internal control of listed companies. This paper empirically tests the effects of ownership concentration, institutional ownership and executive ownership on the internal control of listed companies and their influencing mechanisms. Design/Methodology/Approach Based on the data of all A-shares in Shanghai and Shenzhen stock markets from 2015 to 2019, this paper builds a utility model based on the principal-agent theory, and uses SPSS22.0 software to conduct an empirical study. Findings We find that ownership centralization has a significant positive correlation with effectiveness of internal control, which can appropriately improve the degree of ownership concentration and internal control. There is a significant positive correlation between the institutional shareholding ratio and the executive shareholding ratio and internal control. The listed company can introduce diversified investment subjects to realize the diversified power balance mechanism. Moreover, through the improvement of a good holding ratio, to the greatest extent to meet the income between different shareholders, improve the corporate governance structure, and then improve the effectiveness of internal control. Research Implications Results show that there is no significant correlation between the degree of equity balance and the effectiveness of internal control. Different degrees of checks and balances have different positive effects on the effectiveness of internal control.
A DQN-based Two-Stage Scheduling Method for Real-Time Large-Scale EVs Charging Service
Tianyang Li,Yingnan Han,Xiaolong Li 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.3
With the rapid development of electric vehicles (EVs) industry, EV charging service becomes more and more important. Especially, in the case of suddenly drop of air temperature or open holidays that large-scale EVs seeking for charging devices (CDs) in a short time. In such scenario, inefficient EV charging scheduling algorithm might lead to a bad service quality, for example, long queueing times for EVs and unreasonable idling time for charging devices. To deal with this issue, this paper propose a Deep-Q-Network (DQN) based two-stage scheduling method for the large-scale EVs charging service. Fine-grained states with two delicate neural networks are proposed to optimize the sequencing of EVs and charging station (CS) arrangement. Two efficient algorithms are presented to obtain the optimal EVs charging scheduling scheme for large-scale EVs charging demand. Three case studies show the superiority of our proposal, in terms of a high service quality (minimized average queuing time of EVs and maximized charging performance at both EV and CS sides) and achieve greater scheduling efficiency. The code and data are available at THE CODE AND DATA.
Li Qiqi,Zhang Yingnan,Song Ya,Yang Huawei,Yang Lixia,Bai Liangjiu,Wei Donglei,Wang Wenxiang,Liang Ying,Chen Hou 한국탄소학회 2023 Carbon Letters Vol.33 No.2
Biomass carbon materials with high rate capacity have great potential to boost supercapacitors with cost effective, fast charging–discharging performance and high safety requirements, yet currently suffers from a lack of targeted preparation methods. Here we propose a facile FeCl3 assisted hydrothermal carbonization strategy to prepare ultra-high rate biomass carbon from apple residues (ARs). In the preparation process, ARs were first hydrothermally carbonized into a porous precursor which embedded by Fe species, and then synchronously graphitized and activated to form biocarbon with a large special surface area (2159.3 m2 g−1) and high degree of graphitization. The material exhibited a considerable specific capacitance of 297.5 F g−1 at 0.5 A g−1 and outstanding capacitance retention of 85.7% at 10 A g−1 in 6 M KOH, and moreover, achieved an energy density of 16.2 Wh kg−1 with the power density of 350.3 W kg−1. After 8000 cycles, an initial capacitance of 95.2% was maintained. Our findings provide a new idea for boosting the rate capacity of carbon-based electrode materials.
HUI LI,QUAN LIN,CHUANXI WANG,YINGNAN JIANG,ZHANCHEN CUI 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2014 NANO Vol.9 No.1
Lanthanide-doped luminescent nanoscale materials have great potential applications in biologicalresearches. Herein, we reported a novel and mild method for one-step synthesis of chitosan/NaGdF 4 :Eu 3 þnanocomposites. The luminescent Eu 3 þions and magnetic resonance imaging(MRI) contrast agent Gd 3 þions were incorporated to these biocompatible nanocomposites. Theresultant nanocomposites exhibited strong °uorescence and attractive magnetic features. Thenanocomposites also have pure hexagonal phase with uniform size of about 65 nm. FT-IR spectrarevealed that these nanocomposites were successfully coated by hydrophilic chitosan, whoseamine groups conferred the nanocomposites excellent dispensability in aqueous solution. Besides,the MTT assay and laser confocal microscopy images have con¯rmed the good biocompatibilityof the nanocomposites. These results indicated that the as-prepared nanocomposites could beused as an excellent targeted imaging agent in biological felds.
( Yao Fan ),( Yubo Li ),( Yingnan Shi ),( Shuaishuai Wang ) 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.1
In response to problems such as insufficient extraction information, low detection accuracy, and frequent misdetection in the field of Thangka image defects, this paper proposes a YOLOv5 prediction algorithm fused with the attention mechanism. Firstly, the Backbone network is used for feature extraction, and the attention mechanism is fused to represent different features, so that the network can fully extract the texture and semantic features of the defect area. The extracted features are then weighted and fused, so as to reduce the loss of information. Next, the weighted fused features are transferred to the Neck network, the semantic features and texture features of different layers are fused by FPN, and the defect target is located more accurately by PAN. In the detection network, the CIOU loss function is used to replace the GIOU loss function to locate the image defect area quickly and accurately, generate the bounding box, and predict the defect category. The results show that compared with the original network, YOLOv5-SE and YOLOv5-CBAMachieve an improvement of 8.95% and 12.87% in detection accuracy respectively. The improved networks can identify the location and category of defects more accurately, and greatly improve the accuracy of defect detection of Thangka images.
Xin Fang,Chuang Chen,He Jia,Yingnan Li,Jian Liu,Yisong Wang,Yanli Song,Tao Du,Liying Liu 한국공업화학회 2021 Journal of Industrial and Engineering Chemistry Vol.95 No.-
It is acknowledged as a promising strategy to reduce excessive CO2 emissions by catalytic conversion tovalue-added chemicals, in which layered double hydroxide (LDH) derived catalysts play essential roles. Inthe present review, latest progresses were summarized to gain insights on this issue. LDH-derivedcatalysts can be prepared via various methods and possess favorable characteristics of reversibletopotactic transformation for further development. Compared to conventional catalysts, they showspecific advantages in specific surface area, metal element dispersion and active site distribution. Despiteof distinguished LDH-derived catalysts applied in CO2 reduction reactions to methane, methanol,hydrocarbons, etc., state-of-art LDH-derived catalysts consisted of catalyst-adsorbent synergistic systemare recently constructed to employ the surface CO2 adsorption boundary layer to increase the CO2 partialpressure near active sites for hydrogenation. The overall catalytic performance is thus promoteddramatically. Accordingly, the strategy of adsorption-enhanced hydrogenation is expected to facilitatethe industrialization of CO2 hydrogenation and is instructive for catalyst design in future.