A Comparative Study of Traditional Activity-based Costing and Time-driven Activity-based Costing at a University Hospital in South Korea

Due to serious problems associated with activity-based costing (ABC), time-driven activity-based costing (TDABC) was recently suggested as a better alternative. TDABC is designed to enhance the objectivity of data collection and decrease time and cost as well. However, the accuracy of the allocated costs using TDABC has not been widely tested yet, particularly in the healthcare industry. The objective of this study is to review the differences between ABC and TDABC and apply both techniques to a university hospital in South Korea to evaluate the results of cost allocation of outpatient nursing activities in Pediatrics Department. It is shown that the allocated costs are not very different between the two techniques, which implies that TDABC can be effectively used to compute the cost of each patient accurately. The result of this study also demonstrates that, using TDABC, the visibility of unused resources will be improved at all levels of management in the hospital, and non-value added activities can be managed effectively using the information regarding the activity ratios of traditional ABC and TDABC. In addition, it is shown that the accurate cost report to each doctor can provide a motivation for doctors to improve their profitability. Keywords: Activity-based costing; Time-driven activity-based costing, health care industry DOI: 10.7176/RJFA/10-10-01 Publication date:May 31st 201

Fabrication of n-type nanotube transistors with large-work-function electrodes

The authors found experimentally that carbon nanotube field-effect transistors (CNFETs) could exhibit n -type characteristics even though their electrodes consist of a large-work-function metal such as Co. To explain their result, which is contrary to the general belief that CNFETs with large-work-function electrodes always lead to p -type characteristics, ab initio electronic structure calculation for the metal-carbon nanotube junction was performed, which showed that the Fermi level alignment at the junction could sensitively depend on microscopic structures of the metal-carbon nanotube junction. This suggests that deposition method of electrodes as well as the metal type could be utilized to obtain n -type CNFETs.open121

All-Solution-Processed InGaO 3

We fabricated the crystallized InGaZnO thin films by sol-gel process and high-temperature annealing at 900°C. Prior to the deposition of the InGaZnO, ZnO buffer layers were also coated by sol-gel process, which was followed by thermal annealing. After the synthesis and annealing of the InGaZnO, the InGaZnO thin film on the ZnO buffer layer with preferred orientation showed periodic diffraction patterns in the X-ray diffraction, resulting in a superlattice structure. This film consisted of nanosized grains with two phases of InGaO3(ZnO)1 and InGaO3(ZnO)2 in InGaZnO polycrystal. On the other hand, the use of no ZnO buffer layer and randomly oriented ZnO buffer induced the absence of the InGaZnO crystal related patterns. This indicated that the ZnO buffer with high c-axis preferred orientation reduced the critical temperature for the crystallization of the layered InGaZnO. The InGaZnO thin films formed with nanosized grains of two-phase InGaO3(ZnO)m superlattice showed considerably low thermal conductivity (1.14 Wm−1 K−1 at 325 K) due to the phonon scattering from grain boundaries as well as interfaces in the superlattice grain

Recognition of adherent polychaetes on oysters and scallops using Microsoft Azure Custom Vision

Oyster and scallop cultures have high growth rates in the Korean aquaculture industry. However, their production is declining because of the manual selection of polychaete-adherent oysters and scallops. In this study, an artificial intelligence model for automatic selection of polychaetes was developed using Microsoft Azure Custom Vision to improve the productivity of oysters and scallops. A camera booth was built to capture images of oysters and scallops from various angles. Polychaetes in the images were tagged. Transfer learning available with Custom Vision was performed on the acquired images. By repeating the training and evaluation, the number of training images was increased by analyzing the precision, recall, and mean average precision using the Compact [S1] and General [A1] domains of Custom Vision. This paper presents the artificial intelligence model developed for the automatic selection of polychaete-adherent oysters and scallops as well as the optimal model development method using Microsoft Azure Custom Vision