THE EFFECT OF CR AND DAR ON ROA ON COAL MINING SUB SECTOR COMPANIES LISTED ON BEI

<p>This study aims to determine and analyze how much influence the Return Current Ratio (X1) and Debt to Total Assets Ratio (X2) both simultaneously and partially have on Return On Assets. This research is a quantitative study, using ratio analysis. The analysis method uses multiple linear regression. The results showed that the Current Ratio (X1) and Debt to Total Assets Ratio (X2) simultaneously did not have a significant effect on the Return on Assets of the Coal Mining sub-sector companies listed on the Indonesia Stock Exchange of 0.057. Current Ratio (X1) partially has a significant effect on Return On Assets of 2.369. Debt to Total Assets Ratio (X2) partially has a significant effect on Return On Assets of 2.347.</p><p> </p><p><strong><em>Keywords:</em></strong> CR, DAR, dan ROA<strong></strong></p>

Equation of lightning return current in tropical zones

The purpose of this study is to model mathematically the equation of the lightning return current in tropical zones and to validate it with data obtained from direct measurements in Colombia and Brazil. This study is carried out because the lightning return current in tropical zones is greater than in other latitudes of the planet. For this reason, it is very important for different applications in electrical engineering. Nowadays, the Heidler equation is used for the simulation of faults in electrical systems. This equation will serve as a reference to make a comparison between the data of the equation obtained in this article with those from the equation of Heidler.

Magnetic Field Generation by Charge Exchange in a Supernova Remnant in the Early Universe

We present new-generation mechanisms of magnetic fields in supernova remnant shocks propagating to partially ionized plasmas in the early universe. Upstream plasmas are dissipated at the collisionless shock, but hydrogen atoms are not dissipated because they do not interact with electromagnetic fields. After the hydrogen atoms are ionized in the shock downstream region, they become cold proton beams that induce the electron return current. The injection of the beam protons can be interpreted as an external force acting on the downstream proton plasma. We show that the effective external force and the electron return current can generate magnetic fields without any seed magnetic fields. The magnetic field strength is estimated to be B ∼ 10 − 14 – 10 − 11 G , where the characteristic length scale is the mean free path of charge exchange, ∼ 10 15 cm . Since protons are marginally magnetized by the generated magnetic field in the downstream region, the magnetic field could be amplified to larger values and stretched to larger scales by turbulent dynamo and expansion.

Railway Overhead Contact System Point Cloud Classification

As the railway overhead contact system (OCS) is the key component along the high-speed railway, it is crucial to detect the quality of the OCS. Compared with conventional manual OCS detection, the vehicle-mounted Light Detection and Ranging (LiDAR) technology has advantages such as high efficiency and precision, which can solve the problems of OCS detection difficulty, low efficiency, and high risk. Aiming at the contact cables, return current cables, and catenary cables in the railway vehicle-mounted LiDAR OCS point cloud, this paper used a scale adaptive feature classification algorithm and the DBSCAN (density-based spatial clustering of applications with noise) algorithm considering OCS characteristics to classify the OCS point cloud. Finally, the return current cables, catenary cables, and contact cables in the OCS were accurately classified and extracted. To verify the accuracy of the method presented in this paper, we compared the experimental results of this article with the classification results of TerraSolid, and the classification results were evaluated in terms of four accuracy indicators. According to statistics, the average accuracy of using this method to extract two sets of OCS point clouds is 99.83% and 99.89%, respectively; the average precision is 100% and 99.97%, respectively; the average recall is 99.16% and 99.42%, respectively; and the average overall accuracy is 99.58% and 99.69% respectively, which is overall better than TerraSolid. The experimental results showed that this approach could accurately and quickly extract the complete OCS from the point cloud. It provides a new method for processing railway OCS point clouds and has high engineering application value in railway component detection.