Corporate Governance, Firm Risk, And Corporate Social Responsibility: Evidence From Korean Firms

Given that the prior studies on the relationship between corporate governance structures and firm performance are silent on firms’ social responsible roles, this study introduces an integrated model by combining corporate social responsibility (CSR) and corporate governance structures. This model is used to investigate how CSR moderates the relationship between corporate governance and firm risk in a sample of 640 firm-by-year cases for 215 firms listed on the Korean Stock Exchange between 2005 and 2010. The results show that foreign ownership and board size have a significant and negative relationship with firm risk, whereas management ownership and outside director ratio have no significant effect on firm risk. The results demonstrate that CSR partially moderates the relationship between governance structures (especially management ownership and board size) and firm risk. These findings suggest that Korean firms with concentrated ownership structures can leverage CSR activities as invisible assets to achieve more efficient governance structure model.

A STUDY ON AUTONOMOUS DRIVING ADAPTIVE SIMULATION SYSTEM USING DEEP LEARNING MODEL YOLOV3

For the safety of autonomous vehicles, it is not necessary that the human driver does not have much trouble detecting other vehicles and maintaining a certain distance between them, but in the case of autonomous vehicles, that's not an easy task. The problem of detecting and recognizing the front state of autonomous vehicles is known as object detection by Yolov3 bounding boxes. Therefore, we propose this study to avoid accidents before they occur due to autonomous driving on the road and for a better future.  Our purpose in this study is to put autonomous vehicles on the road in practice using Simulink Matlab, and it is a reflection on the ability of autonomous vehicles to ensure curve road safety And to quickly determine responses on curve road situations such as acceleration/deceleration, stopping, and keeping the same speed direction so that better decisions can be made quickly. Simulation represents a possible solution by enabling the creation of reliable bounding boxes, as a first step, in this study, we discuss the feasibility of a simulation framework to detect the speed of different autonomous vehicles using Yolov3 in the real world. We first developed the YOLOV3 algorithm for autonomous vehicle image recognition using the dataset from the Matlab site. The YOLO v3 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments and in the second part we proposed an effective system using "Vision Vehicle Detector test brake adapter" adaptive HighwayLaneFollowingTestBench/Simulation 3D Scenario to prepare Matlab Simulink simulation environment and sensors, Vision Vehicle Detector. The training parameters are refined through experiments. The vehicle detection rate is approximately 95.8% As per our best knowledge, as a result of the experiment, the proposed system has shown favorable results.For the safety of autonomous vehicles, it is not necessary that the human driver does not have much trouble detecting other vehicles and maintaining a certain distance between them, but in the case of autonomous vehicles, that's not an easy task. The problem of detecting and recognizing the front state of autonomous vehicles is known as object detection by Yolov3 bounding boxes. Therefore, we propose this study to avoid accidents before they occur due to autonomous driving on the road and for a better future.  Our purpose in this study is to put autonomous vehicles on the road in practice using Simulink Matlab, and it is a reflection on the ability of autonomous vehicles to ensure curve road safety And to quickly determine responses on curve road situations such as acceleration/deceleration, stopping, and keeping the same speed direction so that better decisions can be made quickly. Simulation represents a possible solution by enabling the creation of reliable bounding boxes, as a first step, in this study, we discuss the feasibility of a simulation framework to detect the speed of different autonomous vehicles using Yolov3 in the real world. We first developed the YOLOV3 algorithm for autonomous vehicle image recognition using the dataset from the Matlab site. The YOLO v3 model, with an optimal performance compared to the performances of deep learning algorithms, is applied. The training parameters are refined through experiments and in the second part we proposed an effective system using "Vision Vehicle Detector test brake adapter" adaptive HighwayLaneFollowingTestBench/Simulation 3D Scenario to prepare Matlab Simulink simulation environment and sensors, Vision Vehicle Detector. The training parameters are refined through experiments. The vehicle detection rate is approximately 95.8% As per our best knowledge, as a result of the experiment, the proposed system has shown favorable results

Xanthogranulomatous Pancreatitis Combined with Intraductal Papillary Mucinous Carcinoma In Situ

Xanthogranulomatous lesion is a rare condition in which lipid-laden histiocytes are deposited at various locations in the body. Xanthogranulomatous pancreatitis (XGP) associated with an intraductal papillary mucinous tumor (IPMT) is extremely rare. In this study, we described a case of XGP associated with IPMT and include a review of the literature. A pancreatic cystic mass was detected in a 72-yr-old woman by abdominal computed tomography. Pylorus-preserving pancreaticoduodenectomy was performed and diagnosis of XGP combined with intraductal papillary mucinous carcinoma in situ was made. After 13 months of follow-up, the patient is in good health without any evidence of tumor recurrence. Although XGP associated with IPMT is rare, we suggest that such cases should be brought to the attention of clinical investigators, as it may produce clinical features that mimic pancreatic cancer

Breathing silicon anodes for durable high-power operations

Silicon anode materials have been developed to achieve high capacity lithium ion batteries for operating smart phones and driving electric vehicles for longer time. Serious volume expansion induced by lithiation, which is the main drawback of silicon, has been challenged by multi-faceted approaches. Mechanically rigid and stiff polymers (e.g. alginate and carboxymethyl cellulose) were considered as the good choices of binders for silicon because they grab silicon particles in a tight and rigid way so that pulverization and then break-away of the active mass from electric pathways are suppressed. Contrary to the public wisdom, in this work, we demonstrate that electrochemical performances are secured better by letting silicon electrodes breathe in and out lithium ions with volume change rather than by fixing their dimensions. The breathing electrodes were achieved by using a polysaccharide (pullulan), the conformation of which is modulated from chair to boat during elongation. The conformational transition of pullulan was originated from its a glycosidic linkages while the conventional rigid polysaccharide binders have beta linkagesopen1

A hollow sphere secondary structure of LiFePO4 nanoparticles

We report on the evolution of a hollow sphere secondary structure of spherical nanoparticles by a solubilization-reprecipitation mechanism based on the difference of solubility products (K-sp) of two different precipitates. Carbon-coated nanoparticles of olivine structure LiFePO4 served as the primary nano-blocks to build the secondary nano-architecture.close656